M-health apps have developed rapidly and are widely accepted, but users' continued intention to use m-health apps has not been fully explored. This study was designed to obtain a better understanding ...of users' continued intention to use m-health apps. We developed a theoretical model by incorporating the protection motivation theory and network externalities and conducted an empirical study of a 368-respondent sample. The results showed that: (1) perceived vulnerability has a direct impact on users' self-efficacy and response efficacy; (2) self-efficacy and response efficacy have a direct impact on users' attitudes and continued intention; (3) network externalities affect users' attitudes and continued intention, among which direct network externalities have an indirect impact on users' continued intention through attitude; and (4) the impacts of self-efficacy, response efficacy, and indirect network externalities on continued intention are partially meditated by attitudes.
In the current online shopping environment, the virtual nature of transactions and the characteristics of trans-temporal transactions make them subject to certain uncertainties and risks. Trust ...perception is the main means and way to establish a trust relationship between cross-border e-commerce and consumers.. This article analyzes the characteristics of consumers and cross-border e-commerce to build a consumer perception trust evaluation index system from three aspects, and applies the AHP method to determine the final index system. The evaluation results of the K-means algorithm are more objective and can be applied to the problem of consumer perception trust evaluation of cross-border e-commerce. The evaluation results can be used for cross-border e-commerce to intuitively understand consumers’ trust in themselves, and then improve their shortcomings in a targeted manner.
This study investigates how hedonic consumption experiences within a shopping mall dynamically affect the mall's overall sales performance. Using weekly sales data of various products and services ...compiled from 2013 to 2019 by a large shopping mall owner, we examine how customers' spending on certain hedonic services (i.e., food, entertainment, and lifestyle) generates immediate, transient, and long-term sales for other product categories. We develop a conceptual framework based on the customer experience literature to predict these effects. A time-series model is then employed to estimate the dynamic effects empirically. The results indicate that the consumption of hedonic services can induce an immediate increase in the sales of certain product categories, such as apparel and necessities. However, while the transient effects of the consumption of food and lifestyle services appear to be positive, those of entertainment services consumption are either negative or insignificant. Furthermore, customer spending on hedonic services is unlikely to permanently change other product categories' sales. This study provides critical insights into how hedonic shopping experiences influence retailers' short- and long-term sales performance. Further, the study's findings can help retailers anticipate how their investment in hedonic elements would alter overall store performance over time.
This study investigates how promotional activities, mall size, and past shopping experiences affect customer shopping duration, and elucidates how shopping duration induces immediate, transient, and ...long-term effects on customer decisions of whether and how much to buy in the offline shopping mall format. We simultaneously model purchase incidence, purchase amount, and shopping duration to examine empirically the constructs’ dynamic impacts. The proposed model is calibrated using six-year-long transaction data of 43,326 customers that patronize malls operated by a large retail firm. The results reveal that shopping duration leads to an immediate increase in the amount spent by customers in a given period. The transient effects of shopping duration on purchase incidence and amount are significantly positive. However, in the long term, while purchase frequency appears to increase with cumulative shopping duration, purchase amount is negatively affected. Further, shopping duration increases with the size of the mall visited and the level of promotion but tends to converge to a lower level in the long term. Managerial implications for effectively managing customer experience are discussed.
•Investigates the effect of shopping duration on purchase probability and spending.•Examines how shopping duration evolves over time depending on past shopping experiences.•The transient effect of shopping duration on buying behavior is significantly positive.•In the long-term, purchase amount decreases as customers accumulate shopping experiences.•Shopping duration increases with the mall's size and the level of promotion.
There has been an increasing interest to explore and gain knowledge about customer engagement behavior among academia and practitioners. Particularly, the value co-creation process in customer ...services is essential to explore the interaction structure. In this study, we applied the computational simulation of the NK model to identify the value co-creation process between service employees and customers in the service context. To specifically explore the dynamic interaction among them, we identified what kind of service is provided for what type of customers and when service performance improves according to the degree of interaction between service employees and customers. The simulations show that the greatest service value can be achieved when employees and customers jointly perform local search (90%) and long jump (10%). However, if both employees and customers jointly perform local search only, the service value can be stuck in a local optimum. In cases where employees and customers make their independent improvement, either through local search or long jump, the overall service value varies depending on the complexity of interactions between employees and customers. For example, the improvement in service value is the worst when employees and customers make long jumps at independent timings in high complex interactions. Our computational simulations offer visible experimental-based insights into understanding the value co-creation process with customers and promising results for customer service studies.
Textual information retrieval (TIR) is based on the relationship between word units. Traditional word segmentation techniques attempt to discern the word units accurately from texts; however, they ...are unable to appropriately and efficiently identify all new words. Identification of new words, especially in languages such as Chinese, remains a challenge. In recent years, word embedding methods have used numerical word vectors to retain the semantic and correlated information between words in a corpus. In this article, we propose the word-embedding-based method (WEBM), a novel method that combines word embedding and frequent n-gram string mining for discovering new words from domain corpora. First, we mapped all word units in a domain corpus to a high-dimension word vector space. Second, we used a frequent n-gram word string mining method to identify a set of candidates for new words. We designed a pruning strategy based on the word vectors to quantify the possibility of a word string being a new word, thereby allowing the evaluation of candidates based on the similarity of word units in the same string. In a comparative study, our experimental results revealed that WEBM had a great advantage in detecting new words from massive Chinese corpora.
Internet + and Electronic Business in Chinais a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society ...in China.
As an important application in video surveillance, person reidentification enables automatic tracking of a pedestrian through different disjointed camera views. It essentially focuses on extracting ...or learning feature representations followed by a matching model using a distance metric. In fact, person reidentification is a difficult task because, first, no universal feature representation can perfectly identify the amount of pedestrians in the gallery obtained by a multicamera system. Although different features can be fused into a composite representation, the fusion still does not fully explore the difference, complementarity, and importance between different features. Second, a matching model always has a limited amount of training samples to learn a distance metric for matching probe images against a gallery, which certainly results in an unstable learning process and poor matching result. In this paper, we address the issues of person reidentification by the ensemble theory, which explores the importance of different feature representations, and reconcile several matching models on different feature representations to an optimal one via our proposed weighting scheme. We have carried out the simulation on two well-recognized person reidentification benchmark datasets: VIPeR and ETHZ. The experimental results demonstrate that our approach achieves state-of-the-art performance.
Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases, and the number of AD patients has increased year after year with the global aging trend. The onset of AD has ...a long preclinical stage. If doctors can make an initial diagnosis in the mild cognitive impairment (MCI) stage, it is possible to identify and screen those at a high-risk of developing full-blown AD, and thus the number of new AD patients can be reduced. However, there are problems with the medical datasets including AD data, such as insufficient number of samples and different data distributions. Transfer learning, which can effectively solve the problem of distribution discrepancy between training and test data and an insufficient number of target samples, has attracted increasing attention over recent years. In this paper, we propose a multi-source ensemble transfer learning (METL) approach by introducing ensemble learning and our tri-transfer model that uses Tri-Training, which ensures the transferability of source data by the tri-transfer model and high performance through ensemble learning. The experimental results on the benchmark and AD datasets demonstrate that our proposed approach has effective transferability, robustness, and feasibility, and is superior to existing algorithms. Based on METL, we propose an auxiliary diagnosis system for the initial diagnosis of AD, which helps doctors identify patients in the MCI stage as quickly as possible and with high accuracy so that measures can be taken to prevent or delay the occurrence of AD.
•A metaverse platform pricing model is developed to analyze the business model of Real-to-Virtual platforms.•The type and strength of network effects determine the type of bundling strategies.•The ...differences in bundling can reflect the degree of integration of real and virtual businesses.•Real-to-Virtual platforms face a trade-off between maintaining market position and segmenting the virtual and real markets.
This paper aims to investigate the motivations behind the reconstruction of the business model for Real-to-Virtual platforms in the metaverse era. By developing a pricing model for metaverse platforms that consider both social network effects and cross network effects, we analyze the mechanism of network effects on the bundling strategies of Real-to-Virtual platforms. These strategies, in turn, give rise to different business models for metaverse platforms. We find that network effects have a crucial impact on the bundling strategy and business model of Real-to-Virtual platforms. When the social network effects are weak, platforms choose between mixed bundling and independent sales to accommodate both real and virtual businesses, resulting in what we term Digital Twin Platforms. When the cross network effects are relatively weak, platforms may prefer mixed bundling to encompass both types of products, leading to what we term Digital Collaboration Platforms. However, when the two types of network effects are comparable, platforms will adopt a dynamic approach and switch between three strategies to maintain their market position according to the strength of the two network effects and competitive market environment, which is classified as Real-virtual Interconstruction Platforms. Thus, network effects and bundling constitute important aspects that distinguish different Real-to-Virtual platforms, playing a crucial role in platform performance.
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