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


 Movie induced tourism is significant for destination marketing. Although various studies have analyzed movie induced tourism in the past, none of the earlier studies have investigated the movie induced tourism for Bollywood movies. The present study fills the gap and examines the Movie induced tourism for Bollywood movies. The data has been collected for a total of 86 Bollywood movies, and the tourist location it promotes in the movie. The study further employs the event study methodology to understand the influence of the release of the movies on the number of tourists visiting the particular location. The results of the event study suggest that the tourism of a specific place is highly influenced by the movies and thus validated the Movie induced tourism for Bollywood movies. The findings of the study could be used to promote lesser-known regions by portraying them in Bollywood movies and then by subsequent promotions.


Movie Induced Tourism for Bollywood: Event-Study Based Analysis

Using neural network approach, this study revisits the price discovery relationship between spot and futures prices of S&P CNX Nifty Index of India. This study uses minute-by-minute data of 167 trading days (from January 2015 to August 2015). Empirical results reveal that there exists a bi-directional lead-lag relationship between spot and futures markets, but the causality from the spot market to the futures market is more dominant as compared to that of running from the futures market to the spot market. Root mean squared error of the results indicates that the incorporation of spot returns in modelling futures returns improves the results by 62.37%. Whereas, the inclusion of futures returns in modelling spot returns improves the model by only 44.60%. Price discovery dynamics has significant implications for market participants, regulators, and exchanges. These findings could be helpful for portfolio management, market structure design, and the implementation of arbitrage and hedging strategies.

Intraday price discovery in Indian stock index futures market: new evidence from neural network approach

Webometrics can be used for understanding the quantitative aspects of web resources. The present study investigates the role of webometrics in determining the academic ranking of the institute. The extensive research was conducted on a sample of 59 reputed academic institutes based out of India. The data was analysed using two techniques viz. linear regression and classification and regression tree. From the results of the study, it was found that among all the webometrics parameters, Alexa rank and Semrush rank of the website was found to be the most crucial factor for determining the academic ranking of the institute. The study has insights for policymakers of the institute as the results of the study can be used for devising various ways to improve the webometrics parameters in order to enhance the academic ranking of the institute.

Can Webometrics Predict the Academic Rankings of Institutes

This paper attempts to identify the current characteristics of the firms operating out of India and their likely future strategy. It justifies the hypothesis that outsourcing services remain essential, but product companies can flourish. It takes a brief look at the strengths on the services front and then argues that the processes and systems in a services firm make it difficult to operate easily in the product segments. It then touches on the concept of appropriability of an asset and on what complimentary assets are required for firms to emerge successful in the product segments. The risks in this process are 'unbundled' and mechanisms to tackle these risks in the kind of spaces a firm may choose to operate in are discussed. The paper concludes with a roadmap for both service companies and product companies.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID361321_code030108500.pdf?abstractid=361321&mirid=1&type=2

Saurabh Kumar

Papers

Movie Induced Tourism for Bollywood: Event-Study Based Analysis

Intraday price discovery in Indian stock index futures market: new evidence from neural network approach

Can Webometrics Predict the Academic Rankings of Institutes

Strategic Direction for Indian Software Firms - Services vis-a-vis Products