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The purpose of this study was to develop and empirically examine a model of cloud-based human resource information systems (HRIS) adoption by small businesses based on the technology–organization–environment model (Tornatzky & Fleischer, 1990).

This study utilized a survey of 41 small- to medium-sized enterprises in the northeastern United States to examine what HR functions were being supported by cloud-based HRIS and the relationship between three technology factors, three organizational factors, and three environmental factors, and their relationship with the adoption of cloud-based HRIS.

Findings indicated that small businesses are most likely to implement cloud-based HRIS to support day-to-day HR operations. In addition, the findings indicated that top management support (positive), vendor support (positive), and anticipated growth (negative) were each related to organizational adoption of cloud-based HRIS.

The study illustrates how the adoption of a cloud-based HRIS is motivated by different factors than those underlying the adoption of other types of information systems. Executives and small business owners will need to adapt their strategy when considering cloud-based HRIS compared to other types of systems.

Given that small- to medium-sized organizations are the backbone of most global economies, findings from this study can help support society by helping these businesses better understand how to best consider the factors that will support the implementation of cloud-based HRIS.

This chapter represents one of the first to empirically validate a model of the factors affecting adoption of cloud-based HRIS by small businesses.

Generally, the use of cloud-based services has proliferated all around the world. This tendency increased dramatically during the coronavirus disease 2019 (COVID-19) pandemic when many people were obliged to conduct business remotely. This research study aims to investigate the intentions to adopt (IA) cloud-based enterprise resource planning (ERP) systems in Jordan, as this sort of research is scarce in developing countries. Specifically, this research study combined the model of Diffusion of Innovation (DOI) and the Technology-Organization-Environment (TOE) framework, in addition to looking at COVID-19 as a moderating variable.

Data were collected from 60 participants working in Jordanian companies that currently use, or have the intention to use, cloud-based ERP systems. An online questionnaire was developed and used to collect responses. The study hypotheses were examined statistically using simple regression analysis and hierarchical multiple regression.

The study results clearly indicate that complexity, observability, employee information technology (IT) skills, organizational culture (OC) and top management support significantly affect the intention to adopt cloud-based ERP systems. In contrast, relative advantage (RA), compatibility, trialability, regulatory environment and competitive pressure had no significant effect on adoption intention. Interestingly, the advent of COVID-19 increased the intention to adopt cloud-based ERP systems.

The study's findings will provide theoretical and practical suggestions for the effective adoption of cloud ERP in Jordan, as well as aiding other developing nations in comparable situations in implementing cloud-based ERP systems.

The study enriches the knowledge and further examines the underlying reasons for adopting cloud-based ERP systems in a developing country.

The study aims to introduce a cloud-based virtual reality (VR) approach and investigate its applicability and performance in aiding the remote design evaluation process by assessing the clients' convenience perception toward cloud-based VR-aided design evaluation (Cloud-based VR Approach) compared to 3D model-aided design evaluation (3D Model Approach) and rendering images-aided design evaluation (Image Approach).

A multicriteria comparative study was conducted with 26 university students using the “analytic hierarchy process” technique to compare the three approaches applied to home finishing material selection tasks based on the five “service convenience” dimensions, consisting of access convenience, decision convenience, transaction convenience, benefit convenience and post-benefit convenience.

The results indicated that the “Cloud-based VR Approach” was perceived to be more convenient than the “3D Model Approach” and the “Image Approach” based on the aspects of “decision convenience”, “transaction convenience”, “benefit convenience” and “post-benefit convenience”. The only aspect that the Cloud-based VR Approach was comparatively less convenient than the 3D Model Approach and Image Approach for the user was “access convenience”. Overall, the findings showed that the developed Cloud-based VR Approach had more potential than the conventional approaches in aiding the design evaluation process under ongoing social distancing measures requiring designers and clients to work remotely.

The disastrous impacts of the COVID-19 pandemic on logistical systems have resulted in massive disruptions to the construction industry worldwide. Various construction activities have been halted and most meetings moved online. Design evaluation conducted between clients and designers is one of the important activities affected by such an impact. Thus, this study presents the Cloud-based VR Approach as an innovative means to maintain essential ongoing activities and meeting of the current design evaluation approach with respect to the social distancing measures.

The purpose of this study is to examine the factors influencing the usage of cloud-based accounting information systems (AIS) in the crisis era (i.e. the COVID-19 pandemic) by expanding the unified theory of acceptance and use of technology (UTAUT) with new related critical factors.

A quantitative research approach based on a cross-sectional online questionnaire was used for collecting empirical data from 438 potential and current users of cloud-based AIS. Structural equation modeling based on analysis of a moment structures 25.0 was applied in the data analysis.

The outcome of the structural path revealed that performance expectancy, social motivation, COVID-19 risk (COV-19 PR) and trust (TR) were significantly influencing users’ behavioral intention (BI) toward using cloud-based AIS and explained 71% of its variance. While, contrary to what is expected, the impact of effort expectancy and perceived security risk (SEC) on BI was insignificant. In addition, BI was revealed to influence the actual usage behaviors and explained 74% of its variance. The outcome factors: communication quality (CQ) and decision quality (DQ) were significantly influenced by the usage of cloud-based AIS.

The current research would be valuable for small- and medium-sized enterprises officials and policymakers to illustrate the relatively low rates of cloud-based AIS and formulate strategies to boost the acceptance and use of cloud-based AIS by Jordanian users, where cloud-based services are still deemed as an innovation.

To the best of the authors’ knowledge, the current study is the first academic paper that extends the UTAUT by integrating additional factors: TR, SEC and COV-19 PR. In addition to two outcome variables: CQ and DQ, to study the cloud-based AIS in the Jordanian setting beyond the COVID-19 pandemic. The current research contributes to the academic knowledge on information technology information system adoption by considering cloud accounting acceptance and use and integration into the work practices of users through the BIs and actual use of cloud-based AIS in Jordan.

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.

According to expectation–confirmation model (ECM) and task-technology fit (TTF) model, the purpose of this paper is to examine the role of TTF in students’ cloud-based e-learning continuance and evaluate whether TTF affects students’ perceived impact on learning of the cloud-based e-learning system within the educational institution.

Sample data for this study were collected from students enrolled in a comprehensive university in Taiwan. A total of 500 questionnaires were distributed in the campus, and 391 (78.2 percent) usable questionnaires were analyzed using structural equation modeling in this study.

This study’s results verified that both task characteristics and technology characteristics affected students’ perceived TTF, which significantly contributed to their perceived usefulness, confirmation and satisfaction with the cloud-based e-learning system, and these in turn directly or indirectly led to their continuance intention of the system and perceived impact on learning; essentially, the results strongly supported the research model integrating ECM and TTF model via positioning key constructs as the drivers with all hypothesized links being significant.

This study contributes to an understanding of the TTF in explaining students’ cloud-based e-learning continuance that is difficult to explain with only their utilitarian perception of the cloud-based e-learning system, and further places considerably more emphasis upon students’ perceived impact on learning greatly driven by their TTF in the system. Thus, this study’s empirical evidence on incorporating ECM and TTF model can shed light on the outcome for cloud-based e-learning continuance and enhance better understanding of a richer post-adoption model.

The purpose of this study is to propose a research model based on expectation-confirmation model (ECM) to examine whether interactivity and course quality factors (i.e. course content quality, course design quality) as antecedents to student beliefs can influence students' satisfaction and continuance intention of the cloud-based electronic learning (e-learning) system within the educational institution.

Sample data were collected from students enrolled in a comprehensive university in Taiwan. A total of 600 questionnaires were distributed in the campus, and 515 (85.8%) useable questionnaires were analyzed using structural equation modeling.

Findings showed that students' perceptions of interactivity, course content quality and course design quality positively significantly contributed to their perceived usefulness, confirmation and satisfaction with the cloud-based e-learning system, which in turn directly or indirectly led to their continuance intention of the system. Thus, the results strongly supported the research model based on ECM via positioning key constructs as the drivers with all hypothesized links being significant.

This study identifies three factors (i.e. interactivity, course content quality, course design quality) as drivers from the learner perspective within the cloud-based e-learning environment, and links these factors to students' satisfaction and continuance intention of the cloud-based e-learning system based on ECM. It is particularly worth mentioning that the three drivers can serve as precursors for recognizing the determinants that are crucial to understand students' satisfaction and continuance intention of the cloud-based e-learning system. Hence, this study may provide new insights in nourishing the cloud-based e-learning continuance literature in the future.

This study aims to propose an integrated model based on the DeLone and McLean Information System Success Model (DMISS) to examine whether quality factors (system, service and information) can affect user satisfaction and performance of cloud-based marketing systems.

Recently, technologies change quickly, innovation becomes a vital base of productivity and sustainable growth of businesses is widely accepted. Cyber-physical system technologies help industries change production and marketing cycles according to customers’ needs in real-time. In addition, processing information through cloud service helps companies meet customer needs. The advantages of cloud technology also make it easier for companies to quickly collect the latest data from various sources, making it more effective in decision-making. This research recommends cloud-based marketing to help companies maximize their revenue by providing useful information and better quality for business development. The data were gathered from China automotive companies’ customers. A total of 220 questionnaires were distributed, and 165 (82.5%) usable questionnaires were analyzed using structural equation modeling.

This study verified that costumers’ perceived information quality, system quality and service quality positively caused the user satisfaction in the cloud-based marketing system.

This paper presents beneficial advice for improving cloud-based marketing systems. Besides, the topic is relevant to cloud-based marketing systems’ success. A better understanding of the impact of intention to use and user satisfaction on cloud-based marketing systems could significantly enhance companies’ success. This paper’s theoretical and practical contributions are expressed to guide organizations and policymakers in increasing cloud-based marketing systems acceptance.

This study empirically tests the relationship of quality factors and performance outcome of cloud-based marketing system through a model based on DeLone and McLean theory. This study bridges the research gap by identifying the factors that drive the adoption of cloud-based services in marketing and the impact of user satisfaction and intention to use on the cloud-based marketing system performance in the case of china companies.

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.

Cloud computing is a major enabling technology for Industry 4.0 and the Big Data era. However, cloud-based firms, who establish their businesses on cloud platforms, have received scant attention in the extant operations management (OM) literature. To narrow this gap, the purpose of this paper is to investigate cloud-based firms from an operations strategy perspective.

A two-phase multi-method approach was adopted. In the first phase, content analysis of 27 reports from cloud-based firms was conducted, aided by text mining keyword extraction. Two data-related operations capabilities were identified and hypotheses were posited regarding the relationships between data resources (DR), operations capabilities and firm growth (FG). In the second phase, a sample of 190 cloud-based firms was collected. Seemingly unrelated regression and bootstrapping method were employed to test the proposed hypotheses using the survey data.

The content analysis indicates data as a key resource and both data processing capability and data transformational capability as critical operations capabilities of cloud-based firms. FG is regarded as a top priority in the cloud context. The regression results indicate that DR and the two capabilities contribute to the growth of cloud-based firms. Moreover, a follow-up bootstrapping analysis reveals that the mediating effects of the two capabilities vary between different types of FG.

To the authors’ best knowledge, this is one of the first OM studies on cloud-based firms. This study extends the operations strategy literature by identifying and testing the key operations capabilities and priorities of cloud-based firms. It also provides insightful implications for industrial practitioners.