Understanding and predicting students' intention to use mobile cloud storage services

The challenges of mobile devices such as limited bandwidth, computing, and storage have led manufacturers and service providers to develop new value-added mobile services. To address these limitations, mobile cloud computing, which offers on-demand services including platforms, infrastructure, and software have been developed. This study attempts to build a significantly improved research framework based on the Technology Acceptance Model in order to identify factors that affect students' attitudes toward and intentions in using mobile cloud storage services. A structural equation model was used to assess the proposed model based on the data collected from 262 undergraduate students. Results show that perceived usefulness, subjective norm, and trust have a significantly positive effect on the attitude, which in turn is a significant predictor of behavioral intentions. The research model, which explains 82% of the variance in attitudes toward using mobile cloud storage services has a strong predictive power. The findings have both theoretical and practical implications for academics, managers, and educational institutions.     

Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM

This paper reported the results of a survey study and provided evidences of empirically testing a model that integrates both technology acceptance model (TAM) and task-technology fit (TTF) model in understanding the determinants of users’ intention to use wireless technology in organizations. Questionnaires were distributed to organizations that bring mobile commerce into practice through the wireless handheld devices. The results indicated that both technology acceptance model and task-technology fit model are robust models by themselves. First, both perceived usefulness and ease of use significantly influence users’ behavior intention to utilize wireless technology. Perceived ease of use has significant effect on perceived usefulness. Second, characteristics of technology and task significantly predict the fit between these two constructs. Significant effect of characteristics of technology on perceived ease of use and usefulness were observed. Finally, significant relationships between TAM and TTF model were also observed. Task-technology fit is a significant direct predictor of technology adoption intention. Overall, users’ intention to adopt wireless technology in organizations was determined directly by fit between characteristics of task and technology as well as users’ perceived ease of use and usefulness.     

Predicting consumer intention to use mobile service

Abstract. Advances in wireless technology have increased the number of people using mobile devices and accelerated the rapid development of mobile service (m-service) conducted with these devices. However, although many companies are today making considerable investments to take advantage of the new business possibilities offered by wireless technology, research on mobile commerce suggests potential consumers may not adopt these m-services in spite of their availability. Thus, there is a need for research to identify the factors that affect consumer intention to use m-services. Based on the technology acceptance model (TAM), theory of planned behaviour (TPB) and Luarn & Lin's 2005 mobile banking acceptance model, the current research respecifies and validates an integrated model for predicting consumer intention to use m-service by adding one trust-related construct ('perceived credibility') and two resource-related constructs ('self-efficacy' and 'perceived financial resources') to the TAM's nomological structure and re-examining the relationships between the proposed constructs. Data collected from 258 users in Taiwan were tested against the research model using the structural equation modelling approach. The results strongly support the proposed model in predicting consumer intention to use m-service. Several implications for information technology/information system acceptance research and m-service management practices are discussed.     

一种新的云存储服务模型研究*

如何用可生存理论与技术来增强存储服务的可生存性是一个重要的研究内容。本文提出了一种基于云存储的可生存存储服务模型,对模型架构进行了详细设计和构造,并分别采用了一种存储服务模型可用性的度量方法(资源分配失效概率模型)进行了分析,通过该方法分析可得本文提出的可生存云存储服务模型能够满足较高的可生存性的需要。     

Understanding e-learning continuance intention in the workplace: A self-determination theory perspective

Based on self-determination theory (SDT), this study proposed an extended Technology Acceptance Model (TAM) in the context of e-learning service. In the proposed model perceived usefulness, perceived playfulness and perceived ease of use are predicted to be influenced by perceived autonomy support, perceived competence and perceived relatedness. Although TAM has received fairly extensive attention in prior research, this study is one of the first to examine the effects of motivational factors affecting TAM constructs. The results show that applying SDT to e-learning in a work setting can be useful for predicting continuance intention.     

Keeping the user in the cloud: a cognitive social capital antecedent to use continuance and trust-commitment in personal cloud storage services

While personal cloud storage services are popular, providers experience difficulty retaining users. Two explanations of user continuance intention, being use continuance theory and trust-commitment theory, emerge from prior literature. However, there is no unifying theoretical lens to reconcile these two explanations. We use a cognitive social capital explanation to unify these two literature streams. We apply our research model to 237 cloud storage users. We find that the cognitive social capital lens significantly predicts both use continuance intention and trust-commitment in personal cloud storage services. Implications for cloud service providers are offered.     

Determinants of behavioral intention to mobile banking

With the improvement of mobile technologies and devices, banking users are able to conduct banking services at anyplace and at anytime. Recently, many banks in the world have provided mobile access to financial information. The reason to understand what factors contribute to users’ intention to use mobile banking is important issue of research. The purpose of this research is to examine and validate determinants of users’ intention to mobile banking. This research used a structural equation modeling (SEM) to test the causalities in the proposed model.The results indicated strong support for the validity of proposed model with 72.2% of the variance in behavioral intention to mobile banking. This study found that self-efficiency was the strongest antecedent of perceived ease-of-use, which directly and indirectly affected behavioral intention through perceived usefulness in mobile banking. Structural assurances are the strongest antecedent of trust, which could increase behavioral intention of mobile banking. This research verified the effect of perceived usefulness, trust and perceived ease-of-use on behavioral intention in mobile banking. The results have several implications for mobile banking managers.     

Entangled cloud storage

Entangled cloud storage (Aspnes et al., ESORICS 2004) enables a set of clients to “entangle” their files into a single clew to be stored by a (potentially malicious) cloud provider. The entanglement makes it impossible to modify or delete significant part of the clew without affecting all files encoded in the clew. A clew keeps the files in it private but still lets each client recover his own data by interacting with the cloud provider; no cooperation from other clients is needed. At the same time, the cloud provider is discouraged from altering or overwriting any significant part of the clew as this will imply that none of the clients can recover their files.We put forward the first simulation-based security definition for entangled cloud storage, in the framework of universal composability (Canetti, 2001). We then construct a protocol satisfying our security definition, relying on an entangled encoding scheme based on privacy-preserving polynomial interpolation; entangled encodings were originally proposed by Aspnes et al. as useful tools for the purpose of data entanglement. As a contribution of independent interest we revisit the security notions for entangled encodings, putting forward stronger definitions than previous work (that for instance did not consider collusion between clients and the cloud provider).Protocols for entangled cloud storage find application in the cloud setting, where clients store their files on a remote server and need to be ensured that the cloud provider will not modify or delete their data illegitimately. Current solutions, e.g., based on Provable Data Possession and Proof of Retrievability, require the server to be challenged regularly to provide evidence that the clients’ files are stored at a given time. Entangled cloud storage provides an alternative approach where any single client operates implicitly on behalf of all others, i.e., as long as one client’s files are intact, the entire remote database continues to be safe and unblemished.     

保证云存储的数据安全

近几年来,云存储的使用越来越广泛,不仅是中小型企业,而且针对个体用户也可以购买云存储服务,但如何在云存储中保证数据的安全性和机密性,单靠云存储服务供应商的承诺是难以让人信服的。本文提出了一种保证数据安全的云存储框架,并且使用服务等级协议（SLA）作为用户和供应商之间的共同标准。此外,本文还提及了几种保证云存储数据安全的技术,这些技术可以被分成三类：存储保护,传输保护和授权保护。     

Toward an understanding of the behavioral intention to use mobile banking

Although millions of dollars have been spent on building mobile banking systems, reports on mobile banking show that potential users may not be using the systems, despite their availability. Thus, research is needed to identify the factors determining users' acceptance of mobile banking. While there has been considerable research on the technology acceptance model (TAM) that predicts whether individuals will accept and voluntarily use information systems, limitations of the TAM include the omission of an important trust-based construct in the context of electronic/mobile commerce, and the assumption that there are no barriers preventing an individual from using an IS if he or she chooses to do so. Based on literature relating to the theory of planned behavior (TPB) and the TAM, this study extends the applicability of the TAM in a mobile banking context, by adding one trust-based construct (“perceived credibility”) and two resource-based constructs (“perceived self-efficacy” and “perceived financial cost”) to the model, while paying careful attention to the placing of these constructs in the TAM's existing nomological structure. Data collected from 180 users in Taiwan were tested against the extended TAM, using the structural equation modeling approach. The results strongly support the extended TAM in predicting users' intentions to adopt mobile banking. Several implications for IT/IS acceptance research and mobile banking management practices are discussed.     

基于云存储服务的AR博物馆系统研究

针对传统AR博物馆中资源不能有效利用、共享和管理的问题,构建了基于云存储服务的AR博物馆系统。该系统采用FAST-SURF算法对特征点进行检测,通过计算机视觉和无线相结合的方式进行跟踪匹配,同时利用云存储服务技术对博物馆资源进行管理,最终实现虚实融合显示。实例测试表明,与传统AR博物馆相比,该系统能够有效管理博物馆资源,且检测速度快、定位精度较高、匹配性好,特别是在实时性和稳定性方面有明显的优势。结果表明,借助云存储服务技术对AR博物馆资源进行管理的系统方案是可行的,可以为博物馆的研究提供新的技术支持。     

Effects of interface style on user perceptions and behavioral intention to use computer systems

This study examines the influence of two interface styles (menu- and command-based) on the perceived ease of use, perceived usefulness, and behavioral intention of the user to use the system. We have treated the system interface style as an external factor in the technology acceptance model (TAM) to examine its direct and indirect effects on behavioral intention to accept and use a system. The results showed that the interface style had direct effects on perceived ease of use and perceived usefulness which, in turn, demonstrated significant effects on behavioral intention to use the system. Further, the results showed that perceptions of the menu-based interface were more favorable than perceptions of the command-based interface. These results provide several theoretical and practical implications for designing an effective system.     

Students’ expectation,confirmation, and continuance intention to use electronic textbooks

Technology applied to education has produced numerous changes. One of these changes is the availability of electronic textbooks. However, the adoption of electronic textbooks has been slower than anticipated. This research examines adoption of electronic textbooks through studying electronic textbook users’ continuance intentions. Data for the model were collected using a survey of university students. The questionnaire was web-based and distributed using a student listserv. The sample consisted of 469 usable responses received from students who had previously used an electronic textbook. The theoretical model was analyzed using structural equations modeling and maximum likelihood estimation applied to the sample. A confirmatory factor analysis was also performed to evaluate the psychometric properties of the measures for the constructs in the theoretical model. The psychometric properties of the measures were good. The fit of the measured theoretical model to the data was also good and all the paths in the measurement and structural models were statistically significant. The structural model shows that confirmation influences perceived usefulness of electronic textbooks and satisfaction with electronic textbooks. Satisfaction and perceived usefulness of electronic textbooks influence electronic textbook continuance intention. Implications and conclusions based on these findings were also provided.     

A three-phase energy-saving strategy for cloud storage systems

In the running process of cloud data center, the idle data nodes will generate a large amount of unnecessary energy consumption. Furthermore, the resource misallocation will also cause a great waste of energy. This paper proposes a three-phase energy-saving strategy named TPES in order to save energy and operational costs for cloud suppliers. The three phases are replica management based on variable replication factor, cluster reconfiguration according to the optimal total costs and state transition based on observed and predicted workloads. These three phases save energy for the system at different levels which enhance the adaptability of our strategy. We evaluate our strategy using the expanded CloudSim toolkit and the results show that the proposed strategy achieves better energy reduction under different conditions in comparison with the existing schemes.     

Secure searching on cloud storage enhanced by homomorphic indexing

Enterprise cloud tenants would store their outsourced cloud data in encrypted form for data privacy and security. However, flexible data access functions such as data searching is usually sacrificed as a result. Thus, enterprise tenants demand secure data retrieval and computation solution from the cloud provider, which will allow them to utilize cloud services without the risks of leaking private data to outsiders and even service providers.In this paper, we propose an exclusive-or (XOR) homomorphism encryption scheme to support secure keyword searching on encrypted data for cloud storage. First, this scheme specifies a new data protection method by encrypting the keyword and randomizing it by performing XOR operation with a random bit-string for each session to protect access pattern leakage; Secondly, the homomorphic evaluation key enables the searching evaluation to be on-demand calculated, thus it removes the dependency of key storage on cloud and enhance protection against cloud’s violability; Thirdly, this scheme can effectively protect data-in-transit against passive attack such as access pattern analysis due to the randomization. This scheme also can reduce data leakage to service provider because the homomorphism-key solution instead of key storage on cloud. The above three features have been proved by the experiments and further tested out at Email service which can support secure subject searching. The execution time of one searching process is just in the order of milliseconds. We could get 2–3 times speedup compared to default utility grep with the concern of expensive one-time indexing which can be built off-line in advance.     

From cloud computing to cloud manufacturing

Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.     

On the security of auditing mechanisms for secure cloud storage

Cloud computing is a novel computing model that enables convenient and on-demand access to a shared pool of configurable computing resources. Auditing services are highly essential to make sure that the data is correctly hosted in the cloud. In this paper, we investigate the active adversary attacks in three auditing mechanisms for shared data in the cloud, including two identity privacy-preserving auditing mechanisms called Oruta and Knox, and a distributed storage integrity auditing mechanism. We show that these schemes become insecure when active adversaries are involved in the cloud storage. Specifically, an active adversary can arbitrarily alter the cloud data without being detected by the auditor in the verification phase. We also propose a solution to remedy the weakness without sacrificing any desirable features of these mechanisms.     

Using provenance to efficiently improve metadata searching performance in storage systems

In cloud storage systems, more than 50% of requests are metadata operations, and thus the file system metadata search performance has become increasingly important to different users. With the rapid growth of storage system scales in volume, traditional full-size index trees cannot offer high-performance metadata search due to hierarchical indexing bottleneck. In order to alleviate the long latency and improve the quality-of-service (QoS) in cloud storage service, we proposed a novel provenance based metadata-search system, called PROMES. The metadata search in PROMES is split into three phases: (i) leveraging correlation-aware metadata index tree to identify several files as seeds, most of which can satisfy the query requests, (ii) using the seeds to find the remaining query results via relationship graph search, (iii) continuing to refine and rerank the whole search results, and sending the final results to users. PROMES has the salient features of high query accuracy and low latency, due to files’ tight and lightweight indexing in relationship graph coming from provenance’s analysis. Compared with state-of-the-art metadata searching schemes, PROMES demonstrates its efficiency and efficacy in terms of query accuracy and response latency.     

A hybrid solution for privacy preserving medical data sharing in the cloud environment

Storing and sharing of medical data in the cloud environment, where computing resources including storage is provided by a third party service provider, raise serious concern of individual privacy for the adoption of cloud computing technologies. Existing privacy protection researches can be classified into three categories, i.e., privacy by policy, privacy by statistics, and privacy by cryptography. However, the privacy concerns and data utilization requirements on different parts of the medical data may be quite different. The solution for medical dataset sharing in the cloud should support multiple data accessing paradigms with different privacy strengths. The statistics or cryptography technology alone cannot enforce the multiple privacy demands, which blocks their application in the real-world cloud. This paper proposes a practical solution for privacy preserving medical record sharing for cloud computing. Based on the classification of the attributes of medical records, we use vertical partition of medical dataset to achieve the consideration of different parts of medical data with different privacy concerns. It mainly includes four components, i.e., (1) vertical data partition for medical data publishing, (2) data merging for medical dataset accessing, (3) integrity checking, and (4) hybrid search across plaintext and ciphertext, where the statistical analysis and cryptography are innovatively combined together to provide multiple paradigms of balance between medical data utilization and privacy protection. A prototype system for the large scale medical data access and sharing is implemented. Extensive experiments show the effectiveness of our proposed solution.     

Capacity planning for IaaS cloud providers offering multiple service classes

Infrastructure as a Service (IaaS) cloud providers typically offer multiple service classes to satisfy users with different requirements and budgets. Cloud providers are faced with the challenge of estimating the minimum resource capacity required to meet Service Level Objectives (SLOs) defined for all service classes. This paper proposes a capacity planning method that is combined with an admission control mechanism to address this challenge. The capacity planning method uses analytical models to estimate the output of a quota-based admission control mechanism and find the minimum capacity required to meet availability SLOs and admission rate targets for all classes. An evaluation using trace-driven simulations shows that our method estimates the best cloud capacity with a mean relative error of 2.5% with respect to the simulation, compared to a 36% relative error achieved by a single-class baseline method that does not consider admission control mechanisms. Moreover, our method exhibited a high SLO fulfillment for both availability and admission rates, and obtained mean CPU utilization over 91%, while the single-class baseline method had values not greater than 78%.