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The purpose of this paper is to suggest an approach to data value considerations that is related to the generalized notion of authenticity and can be applied to the design of preservation policies. There has been considerable progress in the scalable architectures for policy-driven digital collection preservation as well as in modeling preservation costs. However, modeling the value of both digital artifacts and collections seems a more elusive topic that has yet to find a proper methodology and means of expression.

A top-down conceptual analysis was developed and the principles of information technology service management and quality management were applied to the domain of digital preservation. Then, in a bottom-up analysis, the various notions of authenticity in digital preservation projects, reference models and conceptual papers were reviewed.

The top-down and bottom-up analyses have a meeting point, establishing a close relation between the concepts of data authenticity and data value.

The generalized understanding of authenticity can support the design of sensible preservation policies and their application to the formation and long-term maintenance of digital collections.

This paper proposes and demonstrates that leading digit frequency analysis offers a potential framework to examine the accuracy and authenticity of data values in numerical data sets. Leading digit frequency analysis maintains that the frequencies of leading digits in an original and unmanipulated data set follow a predictable distribution as suggested by Benford’s law. The results of an experiment with random numbers revealed distinct differences between the observed leading digit distribution in the random numbers and the expected distribution as suggested by Benford’s law. This suggests that leading digit frequency analysis offers a useful procedure to evaluate data accuracy and authenticity and offers several useful managerial implications.

Data crawling in e-commerce for market research often come with the risk of poor authenticity due to modification attacks. The purpose of this paper is to propose a novel data authentication model for such systems.

The data modification problem requires careful examinations in which the data are re-collected to verify their reliability by overlapping the two datasets. This approach is to use different anomaly detection techniques to determine which data are potential for frauds and to be re-collected. The paper also proposes a data selection model using their weights of importance in addition to anomaly detection. The target is to significantly reduce the amount of data in need of verification, but still guarantee that they achieve their high authenticity. Empirical experiments are conducted with real-world datasets to evaluate the efficiency of the proposed scheme.

The authors examine several techniques for detecting anomalies in the data of users and products, which give the accuracy of 80 per cent approximately. The integration with the weight selection model is also proved to be able to detect more than 80 per cent of the existing fraudulent ones while being careful not to accidentally include ones which are not, especially when the proportion of frauds is high.

With the rapid development of e-commerce fields, fraud detection on their data, as well as in Web crawling systems is new and necessary for research. This paper contributes a novel approach in crawling systems data authentication problem which has not been studied much.

This paper aims to report that authenticity is a crucial factor in determining consumer behavior. To ensure that customers feel a sense of authenticity, service organizations and researchers must understand the factors that convey perceived authenticity. Therefore, this study uses multiple features of perceived service authenticity (i.e. continuity, consistency, uniqueness, talent, scarcity, honesty and traditional) and examines their effects on positive emotions in a traditional restaurant service context.

A total of 342 surveys were collected using a panel data service in China. This study examined the structural relationships among perceived service authenticity, positive emotions and revisit intention using AMOS. Additionally, a multigroup analysis was conducted to identify the moderating effect of individuals’ psychological traits (i.e. nostalgia proneness).

The results indicated that all service authenticity dimensions except continuity significantly enhance positive emotions. Furthermore, positive emotions significantly enhanced revisit intention. The results also revealed that nostalgia proneness moderates the effects of service authenticity on positive emotions.

The results provide important insights into how to increase customers’ perceived service authenticity and positive emotions.

This research is a pioneering effort to conceptualize a multiple-dimensional structure of the authenticity model and test its appropriateness in a traditional restaurant context. Furthermore, this study is one of the first attempts to develop an authenticity model that links service authenticity, positive emotions, revisit intention and the moderator of nostalgia proneness based on the Mehrabian and Russell model.

The purpose of this paper is to describe personal privacy and data authenticity problems in the cloud and provide solutions to reduce or eliminate them.

This paper reveals issues and applications of anonymity and authenticity in the cloud. It outlines common privacy settings of five web browsers, anonymous browsing on the Tor network and standard methods for verifying the integrity of files obtained in the cloud.

Cloud computing is an emerging technology for libraries which must balance the convenience of ubiquitous access against the potential risks for loss of anonymity and authenticity.

This article contributes to the body of literature on user privacy and data preservation in the cloud and their impact on the library community.

The purpose of this paper is to propose a new deep learning-based model to carry out better maintenance for naval propulsion system.

This model is constructed by integrating different deep learning algorithms. The basic idea is to change the connection structure of the deep neural network by introducing a residual module, to limit the prediction output to a reasonable range. Then, connect the Deep Residual Network (DRN) with a Generative Adversarial Network (GAN), which helps achieve data expansion during the training process to improve the accuracy of the assessment model.

Study results show that the proposed model achieves a better prediction effect on the dataset. The average performance and accuracy of the proposed model outperform the traditional models and the basic deep learning models tested in the paper.

The proposed model proved to be better performed naval propulsion system maintenance than the traditional models and the basic deep learning models. Therefore, our model may provide better maintenance advice for the naval propulsion system and will lead to a more reliable environment for offshore operations.

The purpose of this paper is to compare the plausibility and criticality of two methods of evaluating the implementation of a new government policy within a public service organisation, and to examine the power relations revealed in each evaluation and the social realities of the membership.

Two contrasting approaches to research, based on different theoretical perspectives, were undertaken simultaneously to provide a critical account of an organisation, and its membership, undergoing an externally imposed transformation to improve child protection procedures. The first involved the use of mainly quantitative methods in the form of government sponsored social surveys. Data were triangulated with organisational inspection outcomes. The second method comprised a critical ethnographic evaluation undertaken through discourse analysis in the organisation.

Bottom-up agency rather than top-down structural change is the main influence on policy implementation in child protection. Critical discourse analysis provides a more plausible and credible analysis of the dynamics of organisational change and power relations than surveys.

This research poses new questions over the value of quantitative surveys as opposed to ethnographic methodologies in representing organisational practices.

The purpose of this study is to analyze blockchain (Bc) platforms in order to expand knowledge about the functionalities applied to supply chains (SCs). Therefore, this research seeks to address what these functionalities are and whether they are aligned with the theory.

The research utilizes a literature review on Bc in SC (Bc-SC) and an exploratory study focusing on the developers of Bc platforms.

The research maps and synthesizes concepts and applications of the platforms for SC. The platforms involved with the development of solutions with Bc technology are relatively representative (138 found by this research, and 47 offer solutions in SC), as well as the diversity of solutions and application (101 applications), covering different activities and areas of the SC.

Despite extensive research and data analysis, there may be other platforms and solutions not analyzed in this paper.

The narrative and the different examples provided contribute to managers' understanding of the different possibilities for Bc-SC. Studying the services offered by the various platforms also enables managers to better understand what options exist and could potentially be utilized.

This is one of the first papers in a leading international OSCM (operations and supply chain management) journal to analyze the Bc platforms in SC and thereby has its relevance by synthesizing the knowledge that is dispersed in software developers (platforms).

In recent years, the application of blockchain in enterprise financing has become a hot topic in academic research. This study aims to review the existing literature, construct a knowledge framework for this research topic and propose an agenda for future research.

Based on 181 papers published from 2016 to 2020 in core journal databases in China and abroad, this study used bibliometric tools to identify and analyze an overview of literature publications, research hotspot trends and research theme clustering. This study also qualitatively analyzes literature from the dimensions of enabling mechanisms, multitechnology synergy, challenges, theoretical perspectives and research methods.

This study presents the research progress of blockchain applications in direct financing, bank credit, supply chain finance and other financing modes and analyzes the similarities and differences between domestic and international literature. This study also reveals enabling mechanisms of blockchain in enterprise financing, reflected as information quality improvement (data elements), trust mechanism innovation (business process) and collaboration structure enhancement (network structure). The study found several challenges (e.g. technological uncertainty, data security and organizational change) and trends (e.g. integrated innovation of multiple digital technologies). Additionally, the authors identified several gaps and opportunities for further research.

This study adopts a strict strategy of selecting search terms when retrieving the literature, leading to the exclusion of certain papers on this topic.

This study provides valuable insights into the innovative development of enterprise financing modes enabled by blockchain and emphasizes that managers should clarify the applicable boundaries and necessary conditions of blockchain innovation in different financing scenarios to match technological innovation with industrial expectations.

This study constructs a knowledge framework on this topic based on a comprehensive review of existing research and proposes several important issues for future research based on the identified research gaps.

This study initially aims to identify the barriers to the big data analytics (BDA) initiative and further evaluates the barriers for knowing their interrelations and priority in improving the performance of manufacturing firms.

A total of 15 barriers to BDA adoption were identified through literature review and expert opinions. Data were collected from three types of industries: automotive, machine tools and electronics manufacturers in India. The grey-decision-making trial and evaluation laboratory (DEMATEL) method was employed to explore the cause–effect relationship amongst barriers. Further, the barrier's influences were outranked and cross-validated through analytic network process (ANP).

The results showed that “lack of data storage facility”, “lack of IT infrastructure”, “lack of organisational strategy” and “uncertain about benefits and long terms usage” were most common barriers to adopt BDA practices in all three industries.

The findings of the study can assist service providers, industrial managers and government organisations in understanding the barriers and subsequently evaluating interrelationships and ranks of barriers in the successful adoption of BDA in a manufacturing organisation context.

The paper is one of the initial efforts in evaluating the barriers to BDA in improving the performance of manufacturing firms in India.