Quality antecedents and performance outcome of cloud-based hospital information system continuance intention

Quality factors affecting the continued use of mobile health apps in ethnic minority regions of Southwest China using PLS-SEM and ANN

Mobile technology has significantly accelerated the rapid development of healthcare services. Despite the convenience brought by the proliferation of mobile health (mHealth) apps, the challenge of promoting their continued use among patients has garnered attention from many scholars and administrators. Based on the Expectation Confirmation Model (ECM), this study explores the impact of quality elements on the continuance intention of mHealth apps in Southwest China's ethnic minority regions. Researchers conducted a structured questionnaire survey on 337 users of mHealth apps in these regions to measure their self-reported responses to seven constructs: information quality, system quality, service quality, perceived usefulness, confirmation, satisfaction, and continuance intention. The study uses the structural equation model-artificial neural network (SEM-ANN) approach to interpret the compensatory and non-linear relationships between predictors and continuance intention. The findings reveal that user satisfaction and perceived usefulness significantly predict the continuance intention to use mHealth apps. All other relationships were confirmed except for the non-significant relationships between service quality and confirmation, service quality and perceived usefulness, and system quality and perceived usefulness. Furthermore, based on the normalized importance obtained from the multilayer perceptron, the most critical predictors identified were satisfaction (100%), followed by information quality (70.2%), perceived usefulness (43.2%), system quality (25.1%), and confirmation (17.6%). Finally, this study presents theoretical and practical implications for the continuance intention towards mHealth apps in Southwest China's ethnic minority regions.

Is information evaluated subjectively? Social media has changed the way users search for medical information

Objective

In recent years, social media platforms, such as TikTok and RedBook, have emerged as important channels through which users access and share medical information. Additionally, an increasing number of healthcare professionals have created social media accounts through which to disseminate medical knowledge. This paper explores why users obtain their medical information from social media and how the signals transmitted by social platforms affect use behaviours.

Methods

We combined the elaboration likelihood model and signal theories to construct a comprehensive model for this study. We used simple random sampling to investigate users' behaviours related to social media usage. A total of 351 valid questionnaires were completed by people in Mainland China. The participants were enthusiastic about social media platforms and had searched for health-related information on social media in the past three months. We analysed the data using partial least squares structural equation modelling to investigate the influence of two pathways and two signals (objective and subjective judgement pathways and positive and negative signals) on social media use behaviours.

Results

When seeking medical information on social media, users tend to rely on subjective judgment rather than objective judgment, although both are influential. Furthermore, in the current era, in which marketing methods involving big data algorithms and artificial intelligence prevail, negative signals, such as information overload, have a more pronounced impact than positive signals.

Conclusions

This study demonstrates that the subjective judgment path has a greater impact on users than the objective judgment path. Platforms are encouraged to focus more on users' emotional needs. The paper also discusses the negative impact of information overload on users, sounding an alarm for enterprises to control their use of homogeneous information resulting from the excessive use of big data algorithms.

Do system quality and information quality affect job performance? The mediation role of users' perceptions

This study aims to analyze the influence of the system quality and information quality from the Correctional Database System (CDS) on the job performance of correctional Technical Support Officers (TSO) in Sumatra, either directly or through the mediating role of perceived ease of use and perceived usefulness. This study tested 118 correctional officers selected by the purposive sampling technique. A quantitative approach is used with Partial Least Square-Structural Equation Modeling analysis technique. It was found that the direct influence of the system and information quality on job performance is insignificant. Nonetheless, it shows that perceived ease of use and perceived usefulness fully mediate system quality and information quality on job performance. The findings demonstrate that perceived ease of use and perceived usefulness are important factors that motivate individual performance when supported by high-quality information systems. This study provides an overview of the use of a mandatory system for public organizations and the effects on user performance. It can be used as a basis for correctional institutions to strengthen and maintain a positive perception of CDS among their officers, such as through mentoring programs.

Satisfaction and continuance intention of blended learning from perspective of junior high school students in the directly-entering-socialism ethnic communities of China

BACKGROUND

Blended learning in DESEC only started after the introduction of the internet in recent 5 years. However, there is still no research paying attention to this region, because the area is remote and research subjects are not easily accessible. This article has potential application value in helping the government and educational institutions to make decisions on blended learning strategies supporting poverty alleviation through education in poor and remote areas and ethnic region. The study will be the first to examine satisfaction and continuance intention of blended learning in the DESEC.

OBJECTIVE

To identify junior high students' perception of satisfaction and continuance intention for blended learning in DESEC. To identify the strongest factors affecting junior high students' satisfaction and continuance intention of blended learning in DESEC.

METHODS

A subsample of 635 junior high students participated online survey with consent of their parents verbally in computer room in schools under teacher's instruction. Data was coded and analyzed to generate descriptive statistics and inferential statistics. Structural equation model was used to evaluate the model of satisfaction and continuance intention of blended learning.

RESULTS

The level for evaluating students' agreement on each of item were interpreted "agree" (3.76-3.89). The model explained variances (R2) of Continuance Intention, Satisfaction and Perceived usefulness were 0.665,0.766,0.718 respectively. Information quality, self-efficacy and confirmation directly and indirectly contribute to junior high students' satisfaction with blended learning, which further confirmed their continuance intention of blended learning.

CONCLUSION

Information quality was the strongest factor affecting the junior high students' continuance intention of using blended learning, while confirmation was the strongest factor affecting the junior high students' satisfaction of using blended learning in DESEC. Junior high students do not have a strong and distinct perception on satisfaction and continuance intention for blended learning in DESEC.

Which quality determinants cause MOOCs continuance intention? A hybrid extending the expectation-confirmation model with learning engagement and information systems success

Purpose

The purpose of this study is to propose the research model integrating the expectation-confirmation model with the views of learning engagement (LE) and extending DeLone and McLean information systems (IS) success model to examine whether quality determinants as antecedents to students' beliefs can influence students' continuance intention of massive open online courses (MOOCs).

Design/methodology/approach

Sample data for this study were collected from students enrolled in a comprehensive university in Taiwan. A total of 600 questionnaires were distributed, and 363 (60.5%) useable questionnaires were analyzed using structural equation modeling in this study.

Findings

This study proved that students' perceived knowledge quality, system quality, interface design quality, learner–instructor interaction quality, and collaboration quality all positively caused students' perceived usefulness, confirmation and LE in MOOCs, which jointly explained students' satisfaction with MOOCs and subsequently resulted in students' continuance intention of MOOCs.

Originality/value

This study fully evaluates IS-related and interaction-related quality determinants via an understanding of students' state of LE in explaining students' continuance intention of MOOCs that is difficult to expound with only their utilitarian perception of MOOCs. Hence, this study contributes to deep insights into an all-round quality evaluation in the field of MOOCs continuance intention and takes extrinsic and intrinsic motivators into account in the theoretical development of MOOCs continuance intention to acquire a more comprehensive and robust analysis.

Exploring the Contributing Factors of the Continuance Intention to Use the Mobile Government-to-Employees Services

The purpose of this paper is to evaluate the effects of certain motivational factors on driving the continuance usage intention of mobile government-to-employees services (MG2ES). These services have been frequently overlooked by the extant IT adoption literature in determining the predictors that drive the user’s continuance intention to adopt them. To respond to this lack, an integrated model incorporating factors from several IT adoption theories was developed. These factors were divided into two categories, namely, m-service-centric and user-centric ones. Both categories were specified as direct antecedents of the MG2ES continuance intention. A structured questionnaire-based survey was carried out to empirically examine the hypothesised relationships between the model constructs. The target population of this survey was employees of Saudi’s public sector. The analysis of the collected data (i.e. 194 valid responses) was conducted using the structural equation modelling (SEM) approach. The results demonstrated that only two m-service-centric factors (i.e. m-service strength and effort expectancy) and one user-centric factor (i.e. attitude towards the MG2ES usage) are having positive impacts on the continuance intention to use MG2ES. These findings provide valuable insights and clarifications to the key MG2ES stakeholders about the aspects that motivate such intention, including augmenting the MG2ES strength, implementing effective design mechanisms to reduce the MG2ES usage efforts, delivering more compatible services, and acquiring effective tools for improving information sharing.

How can robo-advisors retain end-users? Identifying the formation of an integrated post-adoption model

Purpose

This study's purpose is to propose an integrated post-adoption model based on expectation-confirmation model (ECM) and cognitive absorption (CA) theory to examine whether network factors, gamification factor, and quality factors as antecedents to end-users' beliefs can affect their continuance intention of the robo-advisor.

Design/methodology/approach

A total of 600 questionnaires were distributed in three sample banks in Taiwan, and sample data for this study were collected from these three banks' customers who had experience in using these banks' own robo-advisor to make their investment decisions. Consequently, 381 useable questionnaires were analyzed using structural equation modeling in this study, with a useable response rate of 63.5%.

Findings

This study proposes a solid research model that based on ECM and CA theory, three types of factors, network factors, gamification factor, and quality factors, as antecedents to end-users’ continuance intention of the robo-advisor have been examined, and this study's results strongly support the research model with all hypothesized links being significant.

Originality/value

This study contributes to end-users' continuance intention of the robo-advisor based on ECM, CA theory, theory of network externalities, gamification, and updated DeLone and McLean IS success model, and reveals deep insights into the evaluation of determinants in the field of end-users' continuance intention of the robo-advisor. Hence, it is especially worth mentioning that three types of determinants (i.e. network factors, gamification factor, and quality factors) are simultaneously evaluated, and extrinsic and intrinsic motivators are both taken into account in this study's research model development of end-users' continuance intention of the robo-advisor to acquire a more all-round and robust analysis.

Digital Transformation Strategies Enabled by Internet of Things and Big Data Analytics: The Use-Case of Telecommunication Companies in Greece

Both Internet of Things (IoT) and Big Data Analytics (BDA) are innovations that already caused a significant disruption having a major impact on organizations. To reduce the attrition of new technology implementation, it is critical to examine the advantages of BDA and the determinants that have a detrimental or positive impact on users’ attitudes toward information systems. This article aims to evaluate the intention to use and the perceived benefits of BDA systems and IoT in the telecommunication industry. The research is based on the Technology Acceptance Model (TAM). Data were collected by 172 users and analyzed using Multivariate Regression Analysis. From our findings, we may draw some important lessons about how to increase the adoption of new technology and conventional practices while also considering a variety of diverse aspects. Users will probably use both systems if they think they will be valuable and easy to use. Regarding BDA, the good quality of data helps users see the system’s benefits, while regarding IoT, the high quality of the services is the most important thing.

Service quality and self-determination theory towards continuance usage intention of mobile banking

Purpose

This research set out to investigate the quality service and self-determination theory (SDT) that contributes to the continuance usage of m-banking.

Design/methodology/approach

A valid of 310 respondents who experienced and intensified using the m-banking is collected. The proposed research model is empirically tested using structural equation modelling.

Findings

The result informs that the service quality can not only be significantly mediated by the SDT, but it also has a direct effect to the satisfaction. It also informs that the satisfaction and the perceived usefulness indeed have a significant effect to the continuance usage intention of the m-banking. In addition, it also demonstrates that the perceived competence and perceived relatedness of the SDT significantly influence satisfaction and perceived usefulness towards the continuance usage intention of the m-banking.

Practical implications

Practically, the results inform the bank management the factors to be taken into account to increase the motivation of the existing customers for them to continue using the m-banking.

Originality/value

Theoretically, these results contribute to the existing literatures of the m-banking by introducing the SDT that determines the continuance usage intention of it.

Tackling post-pandemic challenges with digital technologies: an empirical study

Purpose

This study aims to examine the impact of Covid-19 on social and eco-environmental sustainability. It will also investigate the effect of advanced technologies in the post-pandemic era.

Design/methodology/approach

To get the robust findings, GMM (Generalized Method of Moments) modeling is employed on the panel data of 50 countries across the globe.

Findings

The outcomes indicate that gross fixed capital, logistical operations, knowledge spillover are positive, while Covid-19 is negatively associated with international trade. The results also revealed that Covid-19 spurs poverty and vulnerable employment, while the fertility rate increase creates pressure on economic growth. Also, fossil fuel and energy consumption contribute to carbon emission, while green and advanced technologies may mitigate the environment's adverse effects.

Originality/value

This study is the first of its kind to provide a solution to the challenges posed by the Covid-19 pandemic in the post-pandemic environment. Furthermore, researchers, managers and legislators can use this article's findings to formulate relevant policies for post-pandemic.

Enhancing User Experiences with Cloud Computing via Improving Utilitarian and Hedonic Factors

This study provides insights into the initial and post-adoption of cloud computing services by integrating information technology adoption, social influence, trust, security, and information systems quality theories. Social influence, hedonicity, and automaticity are hedonic predictors of user satisfaction with cloud computing services. Perceived risks, trust in the provider, and system quality are utilitarian predictors of user satisfaction with cloud computing services. The Partial Least Squares (PLS) was employed to test eight hypotheses on the causal relationships between the variables. Six out of eight hypotheses were supported. Hedonic factors appear to have more influence than the utilitarian factor of increasing user satisfaction with cloud computing services in the school setting. The findings lead to both theoretical and practical implications for improving the initial and post-adoption of cloud computing services.

The post-adoption behavior of internet banking users through the eyes of self-determination theory and expectation confirmation model

Purpose

In this era of digital technology, the banking sector has revolutionized its operations by using web-based Internet banking services. However, the success of these financial services is dependent on Internet banking user continuance intention instead of initial adoption. The current study develops a theoretical framework based on three well-known theories, namely the expectation–confirmation theory, self-determination theory (SDT) and the commitment trust theory, to investigate Internet banking user continuance intention towards use of Internet banking services.

Design/methodology/approach

Following positivist paradigm, a research survey was conducted towards Internet banking users of commercial banks. In response, 355 valid observations were retrieved and used for data analysis. For data analysis, this study has used a latest statistical approach, namely structural equation modelling (SEM).

Findings

This study has confirmed that factors underpinning the commitment trust theory, SDT and expectation–confirmation model have significant impact on Internet banking user continuance intention. The research model explained 68.4% of variance in determining Internet banking user continuance intention, which is substantial. The effect size analysis (f2) indicates that perceived usefulness is the most important factor among all other exogenous variables. The predictive relevance of the research model was found substantial Q2 50.3%. These findings confirmed that the research model has substantial power to predict Internet banking user continuance intention.

Practical implications

From a managerial perspective, findings of this research give deeper insight into financial advisors, bank managers and policy- makers to understand human motivation and expectation–confirmation factors in order to retain customers and gain return on Information Technology (IT) investment. Additionally, results suggest that attention should be given on user trust, which in turn boosts user intention towards continuance use of Internet banking services. Extension of the self-determination framework contributes to theory and augments e-commerce literature, especially in a post-adoption setting.

Originality/value

There are several studies that investigate Internet banking user pre-adoption behaviour. Therefore, less is discussed about the Internet banking user’s post-adoption behaviour. Findings of this study help financial advisors to comprehensively understand which factor influences Internet banking user behaviour towards continue use of Internet banking services.

Understanding business analytics continuance in agile information system development projects: an expectation-confirmation perspective

Purpose

This paper seeks to examine how expectations from business analytics (BA) by members of agile information systems development (ISD) teams affect their perceptions and continuous use of BA in ISD projects.

Design/methodology/approach

Data was collected from 153 respondents working in agile ISD projects and analysed using partial least squares structural equation modelling techniques (PLS-SEM).

Findings

Perceived usefulness and technological compatibility are the most salient factors that affect BA continuance intention in agile ISD projects. The proposed model explains 48.4% of the variance for BA continuance intention, 50.6% of the variance in satisfaction, 36.7% of the variance in perceived usefulness and 31.9% of the variance in technological compatibility.

Research limitations/implications

First, this study advances understanding of the factors that affect the continuous use of BA in agile ISD projects; second, it contextualizes the expectation-confirmation model by integrating technological compatibility in the context of agile ISD projects.

Originality/value

This is the first study to investigate BA continuance intention from an employee perspective in the context of agile ISD projects.

Adoption of telemedicine applications among Saudi citizens during COVID-19 pandemic: An alternative health delivery system

This study aims to investigate individual behavior towards adoption of telemedicine application based on wireless sensor network applications (WSN) during COVID-19 pandemic. Therefore, the research model extends the unified theory of acceptance and use of technology with the task technology fit model, awareness and self-efficacy to determine user intention to adopt wireless sensor network application for medical assistance. A research survey was conducted towards Saudi citizens in voluntary setting. In response, 348 valid responses were retrieved. Sample size was computed with prior-power analysis using G-power software. The research model is empirically tested with latest statistical approach namely structural equation modeling (SEM). Findings of the structural model indicate that wireless sensor network application was jointly predicted by performance expectancy, social influence, effort expectancy, facilitating condition, task technology fit, awareness and self-efficacy and explained substantial variance R² 79.5% in individual behavior to adopt wireless sensor network application. The effect size analysis (f²) revealed that task technology fit and facilitating condition were the most important factors to determine user intention to adopt wireless sensor network application. The current study contributes to theory by extending the unified theory of acceptance and use of technology with task technology fit model, self-efficacy and awareness and enrich information system literature in the context of user intention to adopt wireless sensor network application. Practically, this study suggest that health consultant should focus on technology fitness including wireless devices and the number of facilities to operate wireless sensor network which in turn boost user confidence towards adoption of wireless sensor network application for health consultation. This study develops a unique research model that inspects user behavior towards adoption of wireless sensor network technology for better health care system. The findings of this study answer how to monitor COVID-19 while using of wireless sensor network applications. Moreover, this study is useful for clinical management through virtual environment and guides health advisors, software developers and physicians to design user friendly telemedicine application by using wireless sensor network technology.

Investigating medical professionals' continuance intention of the cloud-based e-learning system: an extension of expectation–confirmation model with flow theory

Purpose

The purpose of this study is to propose an integrated model based on expectation–confirmation model (ECM), flow theory and human–organization–technology fit framework to examine whether human, organizational and technology factors as antecedents to medical professionals' beliefs can affect their continuance intention of the cloud-based e-learning system.

Design/methodology/approach

Sample data for this study were collected from medical professionals at five hospitals in Taiwan. A total of 500 questionnaires were distributed, and 368 (73.6%) useable questionnaires were analyzed using structural equation modeling in this study.

Findings

Synthetically speaking, human, organizational and technology factors, as antecedents to medical professionals' continuance intention of the cloud-based e-learning system have been examined, and the results strongly support the research model with all hypothesized links being significant.

Originality/value

Particularly, it is worth mentioning that the application of capturing both ECM and flow theory for completely explaining three types of factors (i.e. human, organizational and technology factors) as external variables to medical professionals' cloud-based e-learning continuance intention is well documented, that is, information systems (IS) and nonIS determinants are simultaneously evaluated, and extrinsic and intrinsic motivators are both taken into consideration in this study's theoretical development of medical professionals' cloud-based e-learning continuance intention to acquire a more comprehensive and robust analysis.