The behavioural intention to adopt Blockchain for supply chain management (BCSCM) is studied in this paper. The research framework adopted considers how Performance Expectancy (PE), Effort Expectan...

Unearthing the determinants of Blockchain adoption in supply chain management

Industry 4.0 ten years on: A bibliometric and systematic review of concepts, sustainability value drivers, and success determinants

Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their user’s data before publishing it to protect the privacy of users whereas anonymous data remains useful for legitimate information consumers. Many anonymization models, algorithms, frameworks, and prototypes have been proposed/developed for privacy preserving data publishing (PPDP). These models/algorithms anonymize users’ data which is mainly in the form of tables or graphs depending upon the data owners. It is of paramount importance to provide good perspectives of the whole information privacy area involving both tabular and SN data, and recent anonymization researches. In this paper, we presents a comprehensive survey about SN (i.e., graphs) and relational (i.e., tabular) data anonymization techniques used in the PPDP. We systematically categorize the existing anonymization techniques into relational and structural anonymization, and present an up to date thorough review on existing anonymization techniques and metrics used for their evaluation. Our aim is to provide deeper insights about the PPDP problem involving both graphs and tabular data, possible attacks that can be launched on the sanitized published data, different actors involved in the anonymization scenario, and major differences in amount of private information contained in graphs and relational data, respectively. We present various representative anonymization methods that have been proposed to solve privacy problems in application-specific scenarios of the SNs. Furthermore, we highlight the user’s re-identification methods used by malevolent adversaries to re-identify people uniquely from the privacy preserved published data. Additionally, we discuss the challenges of anonymizing both graphs and tabular data, and elaborate promising research directions. To the best of our knowledge, this is the first work to systematically cover recent PPDP techniques involving both SN and relational data, and it provides a solid foundation for future studies in the PPDP field.

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Exploring the role of personality, trust, and privacy in customer experience performance during voice shopping: Evidence from SEM and fuzzy set qualitative comparative analysis

Financial institutions, investors, mining companies and related firms need an effective accurate forecasting model to examine gold price fluctuations in order to make correct decisions. This paper proposes an innovative approach to accurately forecast gold price movements and to interpret predictions. First, it compares six machine learning models. These models include two very recent methods: the eXtreme Gradient Boosting (XGBoost) and CatBoost. The empirical findings indicate the superiority of XGBoost over other advanced machine learning models. Second, it proposes Shapley additive explanations (SHAP) in order to help policy makers to interpret the predictions of complex machine learning models and to examine the importance of various features that affect gold prices. Our results illustrate that the utilization of XGBoost along with SHAP approach could provide a significant boost in increasing the gold price forecasting performance.

Forecasting gold price with the XGBoost algorithm and SHAP interaction values

Environmental dynamism, industry 4.0 and performance: Mediating role of organizational and technological factors

Industry 4.0 (I4) technologies are gaining increased importance in the manufacturing industry, as they can provide several benefits such as an increase in efficiency, lower costs, higher revenues, etc. This article empirically examines the critical success factors (CSF) for implementing I4 technologies in Indian automotive manufacturing industry. In this regard, CSF and performance outcomes of I4 technologies are identified from published literature. The relationships between CSF and performance outcomes are then examined by regression analysis. The results indicate that “Data governance” is the most critical factor, as it affects all the four performance outcomes (operational, product, economic, and responsiveness). Similarly, “Legal aspects” affects three out of the four performance outcomes (operational, product, and economic performance), while “Collaboration and teamwork” affects only operational performance and responsiveness. The study provides an understanding of factors which are critical in achieving performance outcomes about I4 technologies in the automotive manufacturing industry. The findings can also help automotive manufacturing firms toward an informed decision making in terms of various strategies required to adopt I4 technologies successfully.

Critical success factors of Industry 4.0 in automotive manufacturing industry

Trust and privacy features of websites have evolved as an important concern for any businesses or interactions, particularly in online networks. The study investigates the relationship between trus...

Interplay between trust, information privacy concerns and behavioural intention of users on online social networks

With the advent of the online social network and advancement of technology, people get connected and interact on social network. To better understand the behavior of users on social network, we need to mine the interactions of users and their demographic data. Companies with less or no expertise in mining would need to share this data with the companies of expertise for mining purposes. The major challenge in sharing the social network data is maintaining the individual privacy on social network while retaining the implicit knowledge embedded in the social network. Thus, there is a need of anonymizing the social network data before sharing it to the third-party. The current study proposes to use upper approximation concept of rough sets for developing a solution for privacy preserving social network graph publishing. The proposed algorithm is capable of preserving the privacy of graph structure while simultaneously maintaining the utility or value that can be generated from the graph structure. The proposed algorithm is validated by showing its effectiveness on several graph mining tasks like clustering, classification, and PageRank computation. The set of experiments were conducted on four standard datasets, and the results of the study suggest that the proposed algorithm would maintain the both the privacy of individuals and the accuracy of the graph mining tasks.

Upper approximation based privacy preserving in online social networks

PurposeThe present study aims to identify and investigate the antecedents of enhanced level of cyber-security at the organisational level from both the technical and the human resource perspective using human–organisation–technology (HOT) theory.Design/methodology/approachThe study has been conducted on 151 professionals who have expertise in dealing with cyber-security in organisations in sectors such as retail, education, healthcare, etc. in India. The analysis of the data is carried out using partial least squares based structural equation modelling technique (PLS-SEM).FindingsThe results from the study suggest that “legal consequences” and “technical measures” adopted for securing cyber-security in organisations are the most important antecedents for enhanced cyber-security levels in the organisations. The other significant antecedents for enhanced cyber-security in organisations include “role of senior management” and “proactive information security”.Research limitations/implicationsThis empirical study has significant implications for organisations as they can take pre-emptive measures by focussing on important antecedents and work towards enhancing the level of cyber-security.Originality/valueThe originality of this research is combining both technical and human resource perspective in identifying the determinants of enhanced level of cyber-security in the organisations.

Saurabh Kumar

Papers

Environmental dynamism, industry 4.0 and performance: Mediating role of organizational and technological factors

Critical success factors of Industry 4.0 in automotive manufacturing industry

Interplay between trust, information privacy concerns and behavioural intention of users on online social networks

Upper approximation based privacy preserving in online social networks

Antecedents for enhanced level of cyber-security in organisations