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The purpose of this paper is to explore the key antecedents of organizational users’ continuance intention of cloud enterprise resource planning (ERP) by the integration of constructs from the expectation–confirmation model (ECM), technology acceptance model (TAM), theory of planned behavior (TPB), task-technology fit and TAM2.

Sample data for this study were collected from end users of cloud ERP working in companies in Taiwan. A total of 560 questionnaires were distributed in the 56 sample companies, and 395 (70.5 percent) usable questionnaires were analyzed using structural equation modeling in this study.

Findings show that while compatibility and output quality are key antecedents of confirmation of expectations toward cloud ERP, compatibility still reveals overwhelming and more indirect impacts on users’ continuance intention of cloud ERP than output quality because output quality has significant but evidently lesser effects on perceived usefulness and perceived ease of use than compatibility.

First, this study contributes to the identification of compatibility and output quality that may reveal deep insights to the task-related factors in understanding users’ cloud ERP continuance greatly driven by their confirmation of expectations toward cloud ERP. Next, the empirical evidence on capturing the ECM, TAM and TPB for completely explaining the antecedents of users’ continued cloud ERP usage intention is well documented. Hence, this study’s findings have significantly shed light on the possible formulation of a richer post-adoption model.

This study's purpose is to propose a hybrid model based on expectation-confirmation model (ECM) and technology acceptance model (TAM) to examine whether organizational users' perceived task-technology fit (TTF) in cloud enterprise resource planning (ERP) as an antecedent to user beliefs can directly and indirectly affect their continuance intention of cloud ERP and individual performance.

Sample data for this study were collected from end users of cloud ERP working in companies in Taiwan. A total of 500 questionnaires were distributed in the 50 sample companies, and 355 (71.0%) usable questionnaires were analyzed using structural equation modeling in this study.

This study showed that organizational users' perceived TTF contributed positively to their perceived usefulness, confirmation and perceived ease of use of cloud ERP, which in turn directly and indirectly led to their satisfaction with cloud ERP, continuance intention of cloud ERP and individual performance; that is, this study's findings strongly supported the research model integrating ECM, TAM and TTF model with all hypothesized links being significant.

This study contributes to an understanding of the TTF model in explaining organizational users' cloud ERP continuance intention that is difficult to explain with only their utilitarian perceptions of cloud ERP. Further, it is especially worth mentioning that this study places considerably more emphasis upon organizational users' individual performance greatly driven by their perceived TTF in cloud ERP and continuance intention of cloud ERP. Thus, this study's empirical evidence on incorporating ECM, TAM and TTF model can significantly enhance better understanding of the outcomes for cloud ERP continuance intention and shed light on the possible formulation of a richer post-adoption model.

This study aims to propose a hybrid model based on expectation–confirmation model (ECM), DeLone and McLean IS success model and task-technology fit (TTF) model to examine whether quality factors and TTF as the antecedents to user beliefs can affect organizational users’ continuance intention of cloud enterprise resource planning (ERP).

Sample data for this study were collected from end-users of cloud ERP working in companies in Taiwan. A total of 370 questionnaires were distributed in the 37 sample companies, and 315 (85.1 per cent) usable questionnaires were analyzed using structural equation modeling in this study.

This study’s findings indicated that users’ perceptions of system quality, information quality and TTF all contributed significantly to their satisfaction, confirmation and perceived usefulness (PU) of cloud ERP, which in turn directly or indirectly led to their continuance intention of cloud ERP; that is, the results strongly supported the hybrid model integrating ECM, DeLone and McLean IS success model and TTF model with all hypothesized links being significant.

Empirically, this study’s research model capturing ECM, DeLone and McLean IS success model and TTF model has significantly shed light on the possible formulation of a richer post-adoption model. Noteworthily, considerably more attention should be paid to the fit between information-related characteristics and cloud ERP users’ work goals and needs, while TTF, based on both system-related and information-related technology characteristics, significantly contributes to users’ satisfaction, confirmation and PU, which together directly or indirectly explain their intention to continue using cloud ERP.

The purpose of this study is to propose an integrated model based on expectation-confirmation model (ECM) and technology acceptance model (TAM) to examine whether environmental, human, organizational and technology factors as the antecedents to physicians’ beliefs can affect their satisfaction and continuance intention of the cloud-based hospital information system (HIS).

Sample data for this study were collected from physicians at six hospitals in Taiwan. A total of 600 questionnaires were distributed, and 471 (78.5 per cent) usable questionnaires were analyzed using structural equation modeling in this study.

This study proposes a solid research model that is based on ECM and TAM, four types of factors, environmental factor, human factor, organizational factors and technology factors, as antecedents to physicians’ continuance intention of the cloud-based HIS have been examined. This study’s results strongly support the research model with all hypothesized links being significant, and control variables have no confounding effects on physicians’ continuance intention of the cloud-based HIS.

This study contributes to physicians’ continuance intention toward the cloud-based HIS based on ECM, TAM, technology-organization-environment framework and human-organization-technology fit framework, and reveals deep insights into the evaluation of determinants in the field of physicians’ continuance intention of the cloud-based HIS. Accordingly, it is particularly worth mentioning that IS and non-IS determinants are simultaneously evaluated and taken into consideration in this study’s theoretical development of physicians’ continuance intention of the cloud-based HIS to acquire a more comprehensive and robust analysis.

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.

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.

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.

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.

This study's purpose is to propose an integrated model based on expectation-confirmation model (ECM), task-technology fit (TTF) model, and updated DeLone and McLean information system (IS) success model to examine whether quality factors and TTF as antecedents to physician beliefs can affect physicians' continuance intention of the cloud-based hospital information system (HIS) and performance impact.

Sample data for this study were collected from physicians at five hospitals in Taiwan. A total of 500 questionnaires were distributed, and 305 (61.0 percent) usable questionnaires were analyzed using structural equation modeling in this study.

This study verified that physicians' perceived information quality, system quality, general technical support service quality, and cloud storage service quality all positively caused their PU, confirmation, and perceived TTF in the cloud-based HIS, which together explained their satisfaction with the system, and subsequently led to their continuance intention of the system and performance impact.

First, IS-related and cloud-related quality factors are simultaneously taken into consideration within this study's research model, and empirical results reveal deep insights into quality evaluation in the field of physicians' cloud-based HIS continuance intention. Next, this study contributes to an understanding of TTF in explaining physicians' cloud-based HIS continuance intention that is difficult to explain with only their utilitarian perception of the system, and places emphasis upon physicians' perception of performance impact greatly driven by their perceived TTF and continuance intention of the system, thus the results can shed light on antecedents and outcome of physicians' cloud-based HIS continuance intention.

Although studies have investigated reasons for software as a service (SaaS) adoption, it is unclear how firm-level SaaS use impacts future SaaS intentions. The purpose of this paper is to propose a theoretical model that integrates the technology-organization-environment framework, institutional theory, diffusion-of-innovation theory and the opportunity-risk framework to analyze the drivers of SaaS use and its continuance intention.

The authors evaluated the direct, moderating and mediating effects of determinants on SaaS continuance intentions using structural equation modeling and data from 301 firms.

Results found that top management support and normative pressures influenced SaaS use. Cost saving and security concerns were direct predictors of perceived opportunities and perceived risks, respectively. Perceived opportunities and risks and actual SaaS use influenced SaaS continuance. Interestingly, perceived opportunities were found to be a negative moderator on the relationship between SaaS use and SaaS continuance.

The results reveal insightful and controversial findings for SaaS research.

This study aims to investigate the impact of skills and knowledge of employees, economic situations of the company, current IT infrastructure, payment fashion, cloud availability, and cloud privacy and security on the productivity of the human resources in the COVID-19 era.

Over the past few years, the advent of cloud-assisted technologies has dramatically advanced the Information Technology (IT)-based industries by providing everything as a service. Cloud computing is recognized as a growing technology among companies around the world. One of the most critical cloud applications is deploying systems and organizational resources, especially systems whose deployment costs are high. Manpower is one of the basic and vital resources of the organization, and organizations need an efficient workforce to achieve their goals. But, in the COVID-19 era, human resources' productivity can be reduced due to stress, high labor force, reduced organizational performance and profits, unfavorable organizational conditions, inability to manage and lack of training. Therefore, this study tries to investigate the productivity of human resources in the COVID-19 era. Data were collected from the medium-sized companies through a questionnaire. Distributed questionnaires were conducted on the Likert scale. The model is assessed using the structural equation modeling technique to examine its reliability and validity. The study is a library method and literature review. A case study was conducted through a questionnaire and statistical analysis by SPSS 25 and SMART-PLS.

Based on the findings, the skills and knowledge of employees, the economic situations of the company, payment fashion, cloud availability and the current IT infrastructures of the company have a positive impact on human resource efficiency in the COVID-19 era. But cloud privacy and security have a negative effect on the productivity of human resources. The findings can be the basis for companies and organizations in the COVID-19 era.

This study has some restrictions that need to be considered in evaluating the obtained results. First, due to the prevalence of Coronavirus, access to information from the companies under study was limited. Second, this research may have overlooked other variables that affect human resource productivity in the COVID-19 era. Prospective researchers can examine the impact of Customer Relationship Management (CRM) and Supply Chain Management (SCM) on the human resource's productivity in the COVID-19 era.

The results of this research are applicable for all companies, their departments and human resources in the COVID-19 era.

In this paper, human resources' productivity in the COVID-19 era is pointed out. The presented new model provides a complete framework for investigating cloud-based enterprise resource planning systems affect the productivity of human resources in the COVID-19 era.

Switching to public cloud enterprise resource planning (ERP) systems not only provides financial and functional benefits to organizations, but also results in sunk costs of incumbent systems and uncertainty costs of cloud systems. The purpose of this study is to investigate the enablers and inhibitors concerning switching to cloud ERP systems at the organizational level.

Data were collected from 212 top managers and owners of the enterprises in Taiwan, and 10 hypotheses were examined using structural equation modeling.

Technological (system quality), organizational (financial advantage), and environmental contexts (industry pressure) are found to be the antecedents of switching benefits. Perceived risk of cloud ERP systems and satisfaction with and breadth of use of incumbent ERP systems are found to be the predictors of switching costs. Switching benefits positively affect switching intention, but switching costs negatively affect switching intention.

This study develops a theoretical model grounded in a set of theoretical foundations, including two-factor theory, technology-organization-environment (TOE) framework, information systems (IS) success model, and expectation confirmation theory (ECT). Two-factor theory is used to characterize switching benefits and costs that affect switching intention. Technological factors come from IS success model, and the factors affecting benefits are organized based on TOE framework. Sunk costs of incumbent ERP systems are developed based on ECT.

Different from previous studies on cloud computing adoption, this study provides insights into switching intention to cloud computing. The study also proposes an integrated model grounded in multiple perspectives to explain organizations' decisions to switch to cloud ERP systems. These findings help cloud service providers better understand how to promote cloud ERP adoption from technical, organizational, and environmental perspectives.

The conditions that are constantly changing and transforming by digital technologies in today's world have forced businesses to think strategically and to comply with the rules and processes of strategic management. Technologies, such as the internet of things, cloud computing, artificial intelligence, and big data, especially in the Industry 4.0 ecosystem, cause some conflicts or constructive and destructive effects on the management approaches and management strategies of businesses. Businesses need to understand these technologies and their effects to maintain their existence and manage their resources and capabilities effectively and strategically. In this chapter, it is aimed to examine the conflicts with destructive and constructive effects of digital technologies on the strategic management of enterprises. For this purpose, the literature was searched qualitatively, and a conceptual study was carried out. At the beginning of the chapter, strategic management literature was researched, and strategic management approaches and views were examined. In the next part, digital technologies in the Industry 4.0 ecosystem are explained. In the last part, digital technologies and their impacts in terms of strategic management approaches (position approach, resource-based approach, and complementing views of resources-based approach) have been examined.