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Although cloud storage services can bring users valuable convenience, they can be technically complex and intrinsically insecure. Therefore, this research explores the concerns of academic users regarding cloud security and technical issues and how such problems may influence their continuous use in daily life.

This qualitative study used a semi-structured interview approach comprising six main open-ended questions to explore the information security and technical issues for the continuous use of cloud storage services by 20 undergraduate students in Hong Kong.

The analysis revealed cloud storage service users' major security and technical concerns, particularly synchronization and backup issues, were the most significant technical barrier to the continuing personal use of cloud storage services.

Existing literature has focused on how cloud computing services could bring benefits and security and privacy-related risks to organizations rather than security and technical issues of personal use, especially in the Asian academic context.

The proliferation of (big) data provides numerous opportunities for service advances in practice, yet research on using data to advance service is at a nascent stage in the literature. Many studies have discussed phenomenological benefits of data to service. However, limited research describes managerial issues behind such benefits, although a holistic understanding of the issues is essential in using data to advance service in practice and provides a basis for future research. The purpose of this paper is to address this research gap.

“Using data to advance service” is about change in organizations. Thus, this study uses action research methods of creating real change in organizations together with practitioners, thereby adding to scientific knowledge about practice. The authors participated in five service design projects with industry and government that used different data sets to design new services.

Drawing on lessons learned from the five projects, this study empirically identifies 11 managerial issues that should be considered in data-use for advancing service. In addition, by integrating the issues and relevant literature, this study offers theoretical implications for future research.

“Using data to advance service” is a research topic that emerged originally from practice. Action research or case studies on this topic are valuable in understanding practice and in identifying research priorities by discovering the gap between theory and practice. This study used action research over many years to observe real-world challenges and to make academic research relevant to the challenges. The authors believe that the empirical findings will help improve service practices of data-use and stimulate future research.

This study/ paper aims to empirically examine the user attitude on perceived security of enterprise systems (ES) mobility. Organizations are adopting mobile technologies for various business applications including ES to increase the flexibility and to gain sustainable competitive advantage. At the same time, end-users are exposed to security issues when using mobile technologies. The ES have seen breaches and malicious intrusions thereby more sophisticated recreational and commercial cybercrimes have been witnessed. ES have seen data breaches and malicious intrusions leading to more sophisticated cybercrimes. Considering the significance of security in ES mobility, the research questions in this study are: What are the security issues of ES mobility? What are the influences of users’ attitude towards those security issues? What is the impact of users’ attitude towards security issues on perceived security of ES mobility?

These questions are addressed by empirically testing a security model of mobile ES by collecting data from users of ES mobile systems. Hypotheses were evolved and tested by data collected through a survey questionnaire. The questionnaire survey was administered to 331 users from Chinese small and medium-sized enterprises (SME). The data was statistically analysed by tools such as correlation, factor analysis, regression and the study built a structural equation model (SEM) to examine the interactions between the variables.

The study results have identified the following security issues: users’ attitude towards mobile device security issues; users’ attitude towards wireless network security issues; users’ attitude towards cloud computing security issues; users’ attitude towards application-level security issues; users’ attitude towards data (access) level security issues; and users’ attitude towards enterprise-level security issues.

The study results are based on a sample of users from Chinese SMEs. The findings may lack generalizability. Therefore, researchers are encouraged to examine the model in a different context. The issues requiring further investigation are the role of gender and type of device on perceived security of ES mobile systems.

The results show that the key security issues are related to a mobile device, wireless network, cloud computing, applications, data and enterprise. By understanding these issues and the best practices, organizations can maintain a high level of security of their mobile ES.

Apart from understanding the best practices and the key issues, the authors suggest management and end-users to work collaboratively to achieve a high level of security of the mobile ES.

This is an empirical study conducted from the users’ perspective for validating the set of research hypotheses related to key security issues on the perceived security of mobile ES.

Master data management (MDM) aims to improve the value of an organization’s most important data, such as customer data, by bridging the silos between organizational units and information systems. However, incorporating data management practices into an organization is not a simple task. The purpose of this paper is to provide a new understanding of the challenges in establishing and developing the MDM function within an organization.

This papers report an ethnographic study within a municipality. The data were collected from two consecutive MDM development projects over the time period of 32 months by observing MDM-related activities and interviewing appropriate actors. Observations, interviews, and impressions were documented to a diary that was later qualitatively analyzed. Various project documentation were also used.

In total 15 challenges were identified. Seven of these were not identified earlier in the literature. New challenges included legislation-driven challenges, mutual understanding of master data domains, and the level of granularity for those domains. Eight issues, such as data owner and data definitions, were MDM specific, others being more generic. All of the issues were identified as preconditions or as affecting factors for the others. Three of the issues were identified as pivotal. The issues emphasize strong alignment between the complex concept of MDM and the organization adopting it.

This research was based on a single qualitative case study, and caution should be exercised with regard to generalizations. The findings increase understanding about the complex organizational phenomena. The study offers public sector and private sector practitioners insights of the organizational issues that establishing a MDM function can encounter.

The issues discovered in the research shed light on the strong alignment between the complex concept of MDM and the organization. The results of this study assist researchers in their endeavor to understand the organizational aspects of MDM, and to build theoretical models, frameworks, practices, and explanations.

This paper aims to examine the nature and extent of disclosure on the use of big data by online platform companies and how these disclosures address and discharge stakeholder accountability.

Content analysis of annual reports and data policy documents of 100 online platform companies were used for this study. More specifically, the study develops a comprehensive big data disclosure framework to assess the nature and extent of disclosures provided in corporate reports. This framework also assists in evaluating the effect of the size of the company, industry and country in which they operate on disclosures.

The analysis reveals that most companies made limited disclosure on how they manage big data. Only two of the 100 online platform companies have provided moderate disclosures on big data related issues. The focus of disclosure by the online platform companies is more on data regulation compliance and privacy protection, but significantly less on the accountability and ethical issues of big data use. More specifically, critical issues, such as stakeholder engagement, breaches of customer information and data reporting and controlling mechanisms are largely overlooked in current disclosures. The analysis confirms that current attention has been predominantly given to powerful stakeholders such as regulators as a result of compliance pressure while the accountability pressure has yet to keep up the pace.

The study findings may be limited by the use of a new accountability disclosure index and the specific focus on online platform companies.

Although big data permeates, the number of users and uses grow and big data use has become more ingrained into society, this study provides evidence that ethical and accountability issues persist, even among the largest online companies. The findings of this study improve the understanding of the current state of online companies’ reporting practices on big data use, particularly the issues and gaps in the reporting process, which will help policymakers and standard setters develop future data disclosure policies.

From these findings, the study improves the understanding of the current state of online companies’ reporting practices on big data use, particularly the issues and gaps in the reporting process – which are helpful for policymakers and standard setters to develop data disclosure policies.

This study provides an analysis of ethical and social issues surrounding big data accountability, an emerging but increasingly important area that needs urgent attention and more research. It also adds a new disclosure dimension to the existing accountability literature and provides practical suggestions to balance the interaction between online platform companies and their stakeholders to promote the responsible use of big data.

Until now, the usage and usability factors of Construction Operation Building information exchange (COBie) datasheet have remained largely overlooked. This oversight may be the potential factor in the lower adoption rates as well as the effective usage of COBie datasheet in the architecture, engineering and construction-facilities management industry. The purpose of this study is to investigate the benefits and key issues associated with COBie datasheet handling and identify the key technological solutions, which can help in mitigating the identified issues.

A literature review was conducted to identify the key benefits of using COBie and issues, which are associated with COBie datasheet handling. This paper has also designed a questionnaire based on a literature review and surveyed professionals who are well versed with handling COBie datasheet. Using responses, the issues are analyzed and discussed using non-parametric statistical analysis.

A total of 9 key benefits and 24 key issues categorized under three groups of usability issues, technical issues and organizational/other issues were identified. The results from the survey agree with all the key issues associated with COBie datasheet handling (with 86 responses). This research also proposes key ideas, that can help in mitigating these issues.

There is a paucity in published literature, which discusses in detail about the various issues associated with COBie datasheet handling. This research study aims to address this gap by identifying key issues by looking at the entire COBie data-capturing process holistically. Finding from this study can help professionals to understand these issues and develop appropriate technological solutions, which can make COBie data capturing and understanding easier. The findings could also assist in increasing the adoption rate of COBie, which could be achieved through mitigation of identified issues.

First of all, I must apologise for the interval between this VINE and the last. Unfortunately VINE's production cycle is growing longer as automated library systems become more complex, and consequently more time‐consuming to write up. Moreover, in this issue I have attempted in certain articles, for instance those on COM bureaux and the Telepen, to adopt a thematic approach to the subject, rather than reporting on individual projects. The process of cross‐checking the details of such articles with all the organisations concerned has been partly responsible for the delay in publishing VINE 17. Nevertheless in the long terms I still hope to increase the frequency with which VINE is published, thereby increasing its currency and decreasing the size of each individual issue.

Access to personal data is key to many of the most successful recent business models. These models rely on individuals outside of traditional organizational boundaries as their product, content providers, and customers. The topic of organizational boundaries is central to organizational research, and these models raise questions about the permeability of these new forms’ boundaries. Herein I elaborate on data-based business models, the organizational field that has emerged around data governance issues, and the institutions that have formed around it at different stages, by various actors. I also explore the interplay of institutional field and organizational boundaries, to identify how field-level issues influence the permeability of organizational boundaries.

Data quality is a critical issue during the implementation of an enterprise resource planning (ERP) system. Data quality problems can have a significant impact on an organisation’s information system. Therefore, it is essential to understand data quality issues to ensure success in implementing ERP systems. This paper uses SAP as an example of an ERP system and describes a study, which explores data quality problems with existing systems, and identifies critical success factors that impact data quality. The study resulted in the development of a framework for understanding data quality issues in implementing an ERP, and application of this framework in a case study in two large Australian organisations. The findings of the study suggest that the importance of data quality needs to be widely understood in implementing an ERP, as well as providing recommendations that may be useful to practitioners.