Who Is out There? Exploring the Effects of Trust and Perceived Risk on SaaS Adoption Intentions

Heart, Tsipi

doi:10.2139/ssrn.2895421

Cited by 13 publications

(26 citation statements)

References 93 publications

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“…Jayatilaka et al (2003) extend this definition and define ASP as an IS application service that is offered, hosted, and managed by a vendor on a rental basis. Researchers argue that SaaS emerged as an advanced form of ASP (Heart, 2010;Benlian and Hess, 2011) and that CC 'has the same key attributes as the "standard" ASP model, and exposes the user to the same risks' (Schwarz et al, 2009: 774). The definitions of ASP and SaaS as well as their similarities in strategic, technical, and economic opportunities and risks (Kern et al, 2002;Schwarz et al, 2009;Armbrust et al, 2010;Marston et al, 2011) support this proposition.…”

Section: Cloud-sourcing Decisionsmentioning

confidence: 99%

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Determinant Factors of Cloud-Sourcing Decisions: Reflecting on the IT Outsourcing Literature in the Era of Cloud Computing

Schneider

Sunyaev

2016

Journal of Information Technology

212

151

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Cloud computing (CC) is an emerging form of IT outsourcing (ITO) that requires organizations to adjust their sourcing processes. Although ITO researchers have established an extensive knowledge base on the determinant factors that drive sourcing decisions from various theoretical perspectives, the majority of research on cloud-sourcing decisions focuses on technological aspects. We reviewed the CC and ITO literature and systematically coded the determinant factors that influence sourcing decisions. We show that most determinant factors of sourcing decisions in the ITO context remain valid for the CC context. However, the findings for some factors (i.e., asset specificity, client firm IT capabilities, client firm size, institutional influences, and uncertainty) are inconclusive for the CC and ITO contexts. We discuss how the peculiarities of CC can explain these inconclusive findings. Our results indicate that CC researchers should draw from research on ITO decision making but re-examine ITO concepts in the light of the peculiarities of CC, such as the differences between software and infrastructure services, the self-service procurement of cloud services, or the evolving role of IT departments. By summarizing determinant factors of cloud-sourcing decisions for consideration in future research, we contribute to the development of endogenous theories in the IS domain.

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Section: Cloud-sourcing Decisionsmentioning

confidence: 99%

“…Market maturity: The maturity of environmental conditions, such as technological maturity (e.g., Kishore et al, 2005) and legal maturity (i.e., the IT-related legal system is well developed and enforced (Qu and Pinsonneauli, 2011)), as well as the number, diversity, and reputation of viable vendors (e.g., Heart, 2010).…”

Section: Environmental Characteristicsmentioning

confidence: 99%

Determinant Factors of Cloud-Sourcing Decisions: Reflecting on the IT Outsourcing Literature in the Era of Cloud Computing

Schneider

Sunyaev

2016

Journal of Information Technology

212

151

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“…Bharadwaj and Lal (2012) found that vendor credibility has a positive influence on adoption of cloud computing through a case study (Bharadwaj & Lal, 2012). Furthermore, Heart (2010) found that vendor credibility has a positive impact on SaaS adoption intention and a negative impact on perceived risk (Heart, 2010). Similarly, Shin (2013) conducted an empirical study whose results showed that security affects cloud computing adoption (Shin, 2013).…”

Section: Perceived Risk and Perceived Trustmentioning

confidence: 99%

Critical Factors for Personal Cloud Storage Adoption in China

Jian-ya

2016

Journal of Data and Information Science

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Purpose: In order to explain and predict the adoption of personal cloud storage, this study explores the critical factors involved in the adoption of personal cloud storage and empirically validates their relationships to a user's intentions.Design/methodology/approach: Based on technology acceptance model (TAM), network externality, trust, and an interview survey, this study proposes a personal cloud storage adoption model. We conducted an empirical analysis by structural equation modeling based on survey data obtained with a questionnaire. Findings:Among the adoption factors we identified, network externality has the salient influence on a user's adoption intention, followed by perceived usefulness, individual innovation, perceived trust, perceived ease of use, and subjective norms. Cloud storage characteristics are the most important indirect factors, followed by awareness to personal cloud storage and perceived risk. However, although perceived risk is regarded as an important factor by other cloud computing researchers, we found that it has no significant influence. Also, subjective norms have no significant influence on perceived usefulness. This indicates that users are rational when they choose whether to adopt personal cloud storage. Research limitations:This study ignores time and cost factors that might affect a user's intention to adopt personal cloud storage.Practical implications: Our findings might be helpful in designing and developing personal cloud storage products, and helpful to regulators crafting policies. Originality/value:This study is one of the first research efforts that discuss Chinese users' personal cloud storage adoption, which should help to further the understanding of personal cloud adoption behavior among Chinese users.

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“…Security of information is the main reason for the slow pace of BDA adoption [36,37]. Perceived trust is a major concern in the BDA acceptance procedure, and thus organizations should generate more trust in BDA adoption [38]. Prior studies by Malaka, Shin, and Sivarajah [23,39,40] also highlighted that perceived security and perceived trust were the biggest challenges and hurdles for BDA acceptance.…”

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confidence: 99%

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

et al. 2019

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IntroductionBig data analytics (BDA) is a course of action to examine large and complex data sets (i.e., big data) and select veiled information that can help organizations with efficient decision making [1]. The volume of data related to healthcare organizations has grown dramatically in past years and is expected to increase in coming years due to the use of innovative technologies [2]. Meanwhile, healthcare reimbursement methods are changing, and pay for performance is an emerging factor in the current healthcare environment. Recently, healthcare organizations have only focused on profit and have neglected to acquire the essential tools, infrastructure, and technologies for effective control of big data to ensure citizens' health care [3,4]. Big data incorporates features such as variety, velocity, and veracity. BDA techniques can be applied to the massive amount of prevailing patient-related medical information to analyze outcomes for improvement of the healthcare sector [5,6]. Using BDA in the healthcare sector will help inform each physician of the medical histories of individuals and the population and enable appropriate Abstract Big data analytics is gaining substantial attention due to its innovative contribution to decision making and strategic development across the healthcare field. Therefore, this study explored the adoption mechanism of big data analytics in healthcare organizations to inspect elements correlated to behavioral intention using the technology acceptance model and task-technology fit paradigm. Using a survey questionnaire, we analyzed 224 valid responses in AMOS v21 to test the hypotheses. Our results posit that the credentials of the technology acceptance model together with task-technology fit contribute substantially to the enhancement of behavioral intentions to use the big data analytics system in healthcare, ultimately leading towards actual use. Meanwhile, trust in and security of the information system also positively influenced the behavioral intention for use. Employee resistance to change is a key factor underlying failure of the innovative system in organizations and has been proven in this study to negatively moderate the relationship between intention to use and actual use of big data analytics in healthcare. Our results can be implemented by healthcare organizations to develop an understanding of the implementation of big data analytics and to promote psychological empowerment of employees to accept this innovative system. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Who Is out There? Exploring the Effects of Trust and Perceived Risk on SaaS Adoption Intentions

Cited by 13 publications

References 93 publications

Determinant Factors of Cloud-Sourcing Decisions: Reflecting on the IT Outsourcing Literature in the Era of Cloud Computing

Determinant Factors of Cloud-Sourcing Decisions: Reflecting on the IT Outsourcing Literature in the Era of Cloud Computing

Critical Factors for Personal Cloud Storage Adoption in China

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

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