User Switching Research Articles

Corrigendum to “Understanding user switch of information seeking: From search engines to generative AI”

Corrigendum to “Understanding user switch of information seeking: From search engines to generative AI”

Can Service Quality Explain Why Users Switch from Free to Premium Services? The Moderating Role of Free Mentality

Can Service Quality Explain Why Users Switch from Free to Premium Services? The Moderating Role of Free Mentality

Exploring Chinese passenger switching intentions toward airline self-service technology from a push–pull–mooring perspective

PurposeAirline self-service technology (SST) has attracted attention from both the academic and aviation sectors. As the use of SST can reduce airlines’ operating costs, investigating SST usage at airports is particularly important for the aviation sector. The extant literature has explored users’ SST usage intention, but users’ switching intentions from traditional manual counter services to SST is still limited. Therefore, to address this issue, we used the push–pull–mooring (PPM) theoretical framework to develop a research model to explore user switching intention.Design/methodology/approachWe utilized a mixed-methods approach. A qualitative approach (i.e., semistructured interviews) was first employed to recognize and choose the candidate factors. Then, we collected 450 valid responses through an online survey to test the model. The partial least squares method was used for data analysis.FindingsWe found that several push (perceived dissatisfaction and perceived inconvenience), pull (perceived ease of use, perceived usefulness and service process fit), and mooring (personal innovativeness and inertia) factors significantly influence switching intention. Additionally, mooring factors exert contextual effects on the relationships between push and switching intentions and between pull factors and switching intentions.Originality/valueThis study contributes to the literature by further increasing our understanding of user switching intentions regarding SSTs from the PPM perspective and offering guidance for the aviation sector to attract and retain customers.

Understanding user switch of information seeking: From search engines to generative AI

Generative AI products such as ChatGPT have changed the way people seek and obtain information. Users may gradually switch from search engines to generative AI for information seeking. This may lead to their defection from search services. Based on the push-pull-mooring (PPM) model, this research examined user switch intention from search engines to generative AI. The results indicated that low information-task fit and information overload lead to user dissatisfaction, whereas AI generated content (AIGC) information quality and perceived interactivity affect perceived value. Dissatisfaction, perceived value, and social influence determine user switch intention. The fsQCA identified three configurations leading to switch intention. The results suggest that search service providers need to enhance information-task fit and reduce information overload in order to retain users and achieve a sustainable development.

Why Do Users Switch to Smart Homes? A Push-Pull-Mooring Framework

Why Do Users Switch to Smart Homes? A Push-Pull-Mooring Framework

SHRIMPS: A framework for evaluating multi-user, multi-modal implicit authentication systems

Smart devices are commonly used in multi-user scenarios, such as shared household devices and shared corporate devices for front-line workers. A multi-user device requires both identification and authentication to defend against unauthorized access and distinguish between legitimate users in real-time, especially when multiple users participate in the same session. Although implicit authentication (IA) has been proposed to provide continuous and transparent authentication throughout a session, most existing IA solutions are optimized for single-user scenarios. The challenges of designing multi-user IA systems include fusing multiple modalities for good accuracy, segmenting and labeling behavioral data while authenticating, and adapting IA models to new users and new incoming data. We propose SHRIMPS, an evaluation framework to support IA researchers in the design of multi-user, multi-modal IA systems. SHRIMPS allows the evaluation of multi-user IA solutions that incorporate multiple modalities and supports adding new users and automatically labeling new incoming data for model updating. SHRIMPS supports different score fusion strategies, including a novel score fusion strategy based on Dempster-Shafer (D-S) theory to improve accuracy with considering uncertainties among different IA mechanisms. SHRIMPS enables composing tasks with public datasets to evaluate and compare different IA schemes. We present and evaluate two sample use cases to showcase how SHRIMPS helps address practical design questions of multi-user, multi-modal IA systems. The evaluation results show that D-S theory based score fusion methods can effectively reject attackers and detect user switches for the multi-user scenario in real-time.

A study of user switching intention for ERP systems based on push-pull-mooring model: Focusing on the important role of information quality for users

Enterprise resource planning (ERP) systems have become indispensable within companies due to their substantial functions and benefits. With a plethora of ERP systems available in the market, management is offered a broad array of options. This paper endeavours to identify the determinants influencing the switching intention of ERP users. Utilizing a conceptual model that adapts the push-pull-mooring paradigm, we seek to construct a formation mechanism of switching intention. Through structural equation modeling conducted on data collected from 236 users, our study uncovers several key findings. The study indicates that system quality, information quality, and top management support exert considerable influence on switching intention via satisfaction. Notably, we found that user satisfaction has a negative impact on switching intention. Our analysis also reveals that alternative attractiveness significantly determines switching intention. These findings provide valuable insights for organizations and ERP vendors to better understand user behaviour and to strategically manage user retention and switching decisions.

Determinants of Switching Behavior of New Energy Vehicles in China: Evidence from Subjective Norms

Purpose - This study investigates the determinants of the switching behavior of new energy vehicles in China. With the help of the Theory of Planned Behavior (TPB) and technology acceptance model (TAM), the moderating effect of subjective norms is discussed.
 Design/Methodology/Approach - We develop and test conceptual models using data collected from 240 respondents in China. Then, SPSS 25.0, AMOS 21.0, PROCESS v3.3, and SmartPLS 4.0.9.2 were used for empirical tests.
 Findings - The results show that perceived risk has a significant negative impact on switching intention, while perceived value and perceived ease of use have a significant positive impact on switching intention. Switching intention has a significant positive impact on user switching behavior. In addition, social identity has a positive correlation effect on attitude toward switching. Moreover, subjective norms have a moderating effect on the relationship between perceived risk, social identity, perceived value, perceived ease of use, and switching intention.
 Research Implications - The theoretical significance of this study lies in the technology acceptance model (TAM), theory of planned behavior (TPB), and the theory of perceived risk, perceived value, and social identity are introduced into the model to expand the model. Its practical significance is to provide feasible suggestions for the new energy automobile industry to meet user expectations, playing a guiding and reference role for the subsequent rise of renewable energy cars, and promoting the outlawing of fuel-powered autos and the sustainable development of renewable energy vehicles.

Application of Improved Sparrow Search Algorithm in Electric Battery Swapping Station Switching Dispatching

The demand for power exchange has risen dramatically as the number of electric vehicle (EV) users has increased, and large-scale disorderly charging will increase the operating costs of battery swapping stations and increase the risk to the power grid. Through experimental comparison with other algorithms on 23 test functions, the results demonstrate that the convergence accuracy and speed of this improved algorithm are superior to those of other algorithms. Furthermore, in solving the optimization problem of EV battery swapping station scheduling, by reasonably allocating the battery pack charging time and establishing the forecasting model of switching demand, the average variance and peak-to-average ratio of the grid are reduced by 29.55% and 13.2%, respectively, based on meeting the user's switching demand. Approximately a 12,500 RMB reduction in the cost of charging lowers the operation cost and risk of battery swapping stations and enhances the user experience.

Automated Gesture-Recognition Solutions using Optimal Deep Belief Network for Visually Challenged People

Gestures are a vital part of our communication. It is a procedure of nonverbal conversation of data which stimulates great concerns regarding the offer of human–computer interaction methods, while permitting users to express themselves intuitively and naturally in various contexts. In most contexts, hand gestures play a vital role in the domain of assistive technologies for visually impaired people (VIP), but an optimum user interaction design is of great significance. The existing studies on the assisting of VIP mostly concentrate on resolving a single task (like reading text or identifying obstacles), thus making the user switch applications for performing other actions. Therefore, this research presents an interactive gesture technique using sand piper optimization with the deep belief network (IGSPO-DBN) technique. The purpose of the IGSPO-DBN technique enables people to handle the devices and exploit different assistance models by the use of different gestures. The IGSPO-DBN technique detects the gestures and classifies them into several kinds using the DBN model. To boost the overall gesture-recognition rate, the IGSPO-DBN technique exploits the SPO algorithm as a hyperparameter optimizer. The simulation outcome of the IGSPO-DBN approach was tested on gesture-recognition dataset and the outcomes showed the improvement of the IGSPO-DBN algorithm over other systems.

Perceived uncertainty and switching intention on e-commerce platforms: The moderating role of usage habit

With the increasingly fierce competition between e-commerce platforms, the issue of user retention has received more attention. Prior research highlighted the important role of perceived uncertainty in predicting user switching. This study enriches this line of literature by incorporating the effect of habit. Distinguished from other research, our study specifically focuses on the conflict between habit, which requires a stable environment for formation, and perceived uncertainty, which indicates an unstable environment. Anchored on the stimulus–organism–response framework, we provide a clear explanation of how perceived uncertainty interacts with habit and shapes users’ switching intention in the e-commerce context. An online survey of 358 users of the Taobao in China provided data for model validation and partial least squares estimation was adopted for data analysis. Three main findings emerged. First, perceived information asymmetry and perceived seller opportunism increase users’ perceived uncertainty. Second, perceived uncertainty directly and positively influences disconfirmation (internal cognition), whereas indirectly influences dissatisfaction (internal affect) through disconfirmation. Third, users’ habit of using an incumbent platform negatively moderates the effect of perceived uncertainty on disconfirmation. Furthermore, both disconfirmation and dissatisfaction positively contribute to the users’ switching intention. The findings provide valuable enlightenment for e-commerce platform owners to retain users.

Image analysis considering textual correlations enables accurate user switching tendency prediction

Image analysis considering textual correlations enables accurate user switching tendency prediction

Implementasi Analisis Markov pada R Studio untuk Model Prediksi Perpindahan Pengguna Transportasi Online

The development of transportation in Indonesia has entered an era of collaboration with information technology. Online-based transportation has proven to facilitate the mobility of people's lives. The emergence of various online transportation providers in Indonesia requires these providers to have data-based programmed business planning. Predicting customer loyalty is one of the factors considered in business planning. This study aims to predict the switching behavior of online transportation users using Markov Analysis. The study uses data taken from 100 respondents in Jakarta. User switching patterns are analyzed based on the first, second, and third months of online transportation providers used by the respondents. Gojek and Grab are used as the online transportation providers examined in this study. This research resulted in a Steady State, indicating that the user loyalty for Gojek from November 2022 to January 2023 was 66%, while Grab accounted for 34%.

Product Marketing Diffusion Under the Background of Digital Economy Based on Improved Bass Model

Abstract Based on the improved Bass model, this paper studies online social platforms’ product information dissemination mode. This paper studies the Bass model based on the attenuation effect of user benefits. The dissemination mode of product information is established. It is the effect of product information quality on product information dissemination. Finally, an empirical analysis is made of the communication materials of the films released from 2013 to 2022. It is found that the user switching engine and the user’s consumption have obvious effects on the information transmission. This model has better prediction accuracy and fitting performance than the Bass model. This model can be applied to the calculation of other damped transport rates.

To switch or not? Effects of spokes-character urgency during the social app loading process and app type on user switching intention.

Users of mobile phone applications (apps) often have to wait for the pages of apps to load, a process that substantially affects user experience. Based on the Attentional Gate Model and Emotional Contagion Theory, this paper explores the effects of the urgency expressed by a spokes-character's movement in the loading page of a social app the app type on users' switching intention through two studies. In Study 1 (N = 173), the results demonstrated that for a hedonic-orientated app, a high-urgency (vs. low-urgency) spokes-character resulted in a lower switching intention, whereas the opposite occurred for a utilitarian-orientated app. We adopted a similar methodology in Study 2 (N = 182) and the results showed that perceived waiting time mediated the interaction effect demonstrated in Study 1. Specifically, for the hedonic-orientated (vs. utilitarian-orientated) social app, the high-urgency (vs. low-urgency) spokes-character made participants estimate a shorter perceived waiting time, which induces a lower user switching intention. This paper contributes to the literature on emotion, spokes-characters, and human-computer interaction, which extends an enhanced understanding of users' perception during loading process and informs the design of spokes-characters for the loading pages of apps.

Examining user switching between social Q&A platforms: a push–pull-mooring perspective

Examining user switching between social Q&A platforms: a push–pull-mooring perspective

Influencing factors of young people’s short video switching behaviour based on grounded theory

PurposeThis study aims to explore the switching behaviour of short video (SV) users and its influencing factors and promote the sustainable development of SV platforms (SVPs) and the marketing strategy formulation of library and information institutions.Design/methodology/approachUsing the qualitative research method of semi-structured interviews and grounded theory, this study conducts an exploratory study on the user switching phenomenon of an SVP. The authors encoded the interview text at three levels, extracted the factors influencing user switching behaviour on an SVP and constructed the corresponding theoretical model.FindingsThis study identifies the following major internal and external factors influencing user switching behaviour of SVP: platform quality, social environment, individual characteristics and use needs. It also elaborates on the impact of these internal and external factors on user switching behaviour.Originality/valueThis study explored the factors influencing SV user switching behaviour and constructed corresponding theoretical models, enriching research in information technology and social media switching. In practice, this study helped the existing SVPs and library and information institutions establish a corresponding early warning mechanism to prevent the loss of existing users and attract new users.

Trust Beyond Border: Lightweight, Verifiable User Isolation for Protecting In-Enclave Services.

Due to the absence of in-enclave isolation, today's trusted execution environment (TEE), specifically Intel's Software Guard Extensions (SGX), does not have the capability to securely run different users' tasks within a single enclave, which is required for supporting real-world services, such as an in-enclave machine learning model that classifies the data from various sources, or a microservice (e.g., data search) that performs a very small task (within sub-seconds) for a user and therefore cannot afford the resources and the delay for creating a separate enclave for each user. To address this challenge, we developed Liveries, a technique that enables lightweight, verifiable in-enclave user isolation for protecting time-sharing services. Our approach restricts an in-enclave thread's privilege when configuring an enclave, and further performs integrity check and sanitization on critical enclave data upon user switches. For this purpose, we developed a novel technique that ensures the protection of sensitive user data (e.g., session keys) even in the presence of the adversary who may have compromised the enclave. Our study shows that the new technique is lightweight (1% overhead) and verifiable (about 3200 lines of code), making a step towards assured protection of real-world in-enclave services.

User switching intention from E-marketplace to E-pharmacy: The Influence of push, pull, and mooring factors

The COVID-19 pandemic triggered a shift in medicine purchasing behaviors worldwide, including in Indonesia, from physical pharmacies to online platforms such as electronic marketplaces (e-marketplaces) and electronic pharmacies (e-pharmacies). However, shopping for drugs through e-marketplaces does not always guarantee quality and safety, especially since drugs are high-risk products. Considering this issue, the present study was conducted to determine the factors that can motivate Indonesian people to switch to e-pharmacies, which are directly managed by the pharmacies themselves and thus offer safe processes and products. The push–pull–mooring theory and the DeLone and McLean information system success model were used in the study. Questionnaire data were collected from 778 respondents, and 30 respondents were in-depth interviewed. Partial least square structural equation modeling was conducted to process the quantitative data obtained, and a content analysis was used for the qualitative data. The research findings showed that push, pull, and mooring factors influence the intention to switch to an e-pharmacy, in turn affecting actual switching behaviors. The results provide valuable insights into how e-marketplaces can deliver the quality and security expected by their users, with regulators to provide strict regulations, and how e-pharmacies can strengthen their features for added value over e-marketplaces.

Satisfaction to Stay, Regret to Switch: Understanding Post-adoption Regret in Choosing Competing Technologies When Herding

Facing hundreds of similar alternatives in any technology adoption decision, users commonly take a shortcut in the decision making: following others in the herd! Although herding is found to be an influential force for technology adoption, this research sheds light on its distal on user staying power in post-adoption evaluations. Supplementing the dominant perspective that users will stay with the chosen technology when satisfactory with the technology performance, this research proposes a new regret perspective to account for users’ concerns about the competing technologies and the decision process in the technology adoption decision. A research model is accordingly constructed and tested in two longitudinal field studies on user adoption and post-adoption evaluations across competing technologies in both forms of free software and paid hardware, with samples collected in Asia and Europe. Based on the findings and insights, this research suggests to IT vendors on how to retain users in the new era with competing technologies by understanding how post-adoption regret, the predominant factor of user switching, is formed and how it can be lessened.