Smart monitoring and controlling of government policies using social media and cloud computing

YesThe governments, nowadays, throughout the world are increasingly becoming dependent on public opinion regarding the framing and implementation of certain policies for the welfare of the general public. The role of social media is vital to this emerging trend. Traditionally, lack of public participation in various policy making decision used to be a major cause of concern particularly when formulating and evaluating such policies. However, the exponential rise in usage of social media platforms by general public has given the government a wider insight to overcome this long pending dilemma. Cloud-based e-governance is currently being realized due to IT infrastructure availability along with mindset changes of government advisors towards realizing the various policies in a best possible manner. This paper presents a pragmatic approach that combines the capabilities of both cloud computing and social media analytics towards efficient monitoring and controlling of governmental policies through public involvement. The proposed system has provided us some encouraging results, when tested for Goods and Services Tax (GST) implementation by Indian government and established that it can be successfully implemented for efficient policy making and implementation

Google search trends and stock markets: sentiment, attention or uncertainty?

Keyword based measures purporting to reflect investor sentiment attention or uncertainty have been increasingly used to model stock market behaviour. We investigate and shed light on the narrative reflected by Google search trends (GST) by constructing a neutral and general stock market-related GST index. To do so we apply elastic net regression to select investor relevant search terms using a sample of 77 international stock markets. The index peaks around significant events that impacted global financial markets moves closely with established measures of market uncertainty and is predominantly correlated with uncertainty measures in differences implying that GST reflect an uncertainty narrative. Returns and volatility for developed emerging and frontier markets widely reflect changing Google search volumes and relationships conform to a prior expectations associated with uncertainty. Our index performs well relative to existing keyword-based uncertainty measures in its ability to approximate and predict systematic stock market drivers and factor dispersion underlying return volatility both in-sample and out-of-sample. Our study contributes to the understanding of the information reflected by GST their relationship with stock markets and points towards generalisability thus facilitating the development of further applications using search and return data

Sentiment analysis of patient feedback

The application of sentiment analysis as a method for the automatic categorisation of opinions in text has grown increasingly popular across a number of domains over the past few years. In particular, health services have started to consider sentiment analysis as a solution for the task of processing the ever-growing amount of feedback that is received in regards to patient care. However, the domain is relatively under-studied in regards to the application of the technology, and the effectiveness and performance of methods have not been substantially demonstrated. Beginning with a survey of sentiment analysis and an examination of the work undertaken so far in the clinical domain, this thesis examines the application of supervised machine learning models to the classification of sentiment in patient feedback. As a starting point, this requires a suitably annotated patient feedback dataset, for both analysis and experimentation. Following the construction and detailed analysis of such a resource, a series of machine learning experiments study the impact of different models, features and review types to the problem. These experiments examine the applicability of the selected methods and demonstrate that model and feature choice may not be a significant issue in sentiment classification, whereas the type of review that the models train and test across does affect the outcome of classification. Finally, by examining the role that responses play in the patient feedback process and developing the idea of incorporating the inter-document context provided by the response into the feedback classification process, a recalibration framework for [continued…

The impact of customer satisfaction on purchase intention in Malaysian takaful industry

To date the study of customer satisfaction and purchase intention have dominated the services literature. This study is aimed to investigate the impact of customer satisfaction on purchase intention among Takaful participants in Malaysia. A self-administered questionnaire is distributed to eight Takaful companies in Malaysia as a study setting for this study. Out of the total 600 distributed questionnaires 390 were finally selected for data analyses. It is expected that findings from this study will contribute to the existing literature to both theoretical and managerial approaches in order to better understand the pattern of customer satisfaction and purchase intention in Takaful industry settings

Designing behavior change support systems in the context of knowledge documentation: development of theory and practical implementation

Although innovation and operating efficiently require creating, transferring, and applying knowledge, successful knowledge documentation remains a challenge for organizations. While knowledge management systems support knowledge management activities, the missing link to applying knowledge management relies on human actions and their behaviors. This dissertation extends prior design knowledge about designing Behavior Change Support Systems in the context of knowledge documentation by developing theory and showing practical implementation. Combining technical and psychological models within information systems frameworks based on the principles of abstraction, originality, justification, and benefit, this dissertation draws on design science to propose prescriptive knowledge, for example, in the form of design principles and a specific artifact

The tax morale of individual taxpayers in Indonesia

This research investigates the tax morale of individual taxpayers in Indonesia, employing a mixed methodology approach, utilising survey and interviews as the methods in obtaining the data. The results show individual taxpayers in Indonesia have a high level of tax morale, where perceptions of the legal system and sentiment towards tax are statistically significant factors in influencing the level of tax morale. Age, education level, and financial condition are also found to affect tax morale

Proceedings of the 2nd Conference on Managing Digital Industry, Technology and Entrepreneurship (CoMDITE 2021)

This conference proceeding provides the compilation of all papers presented during the 2nd Conference on Managing Digital Industry, Technology and Entrepreneurship (CoMDITE 2021) on 7th and 8th April 2021. This year, CoMDITE is held virtually with participations from local and international participants. The theme is Business Sustainability Through Digital Transformation. CoMDITE 2021 is mainly aimed to serve as a sharing platform that enables researchers, academics and practitioners to share the best practices, which have been produced through research, as well as to potentially propose the best strategy in utilizing digital transformation for business sustainability. All papers are presented according to the following seven tracks - (i) Big Data Analytics for Business, (ii) Digital Innovative and Education, (iii) Digital Marketing, (iv) Digital Talent for Management, (v) Digital Technology for Business, (vi) Entrepreneurship and (vii) Strategic Management and Ecosystem Business

Graphs behind data: A network-based approach to model different scenarios

openAl giorno d’oggi, i contesti che possono beneficiare di tecniche di estrazione della conoscenza a partire dai dati grezzi sono aumentati drasticamente. Di conseguenza, la definizione di modelli capaci di rappresentare e gestire dati altamente eterogenei è un argomento di ricerca molto dibattuto in letteratura. In questa tesi, proponiamo una soluzione per affrontare tale problema. In particolare, riteniamo che la teoria dei grafi, e più nello specifico le reti complesse, insieme ai suoi concetti ed approcci, possano rappresentare una valida soluzione. Infatti, noi crediamo che le reti complesse possano costituire un modello unico ed unificante per rappresentare e gestire dati altamente eterogenei. Sulla base di questa premessa, mostriamo come gli stessi concetti ed approcci abbiano la potenzialità di affrontare con successo molti problemi aperti in diversi contesti. ​Nowadays, the amount and variety of scenarios that can benefit from techniques for extracting and managing knowledge from raw data have dramatically increased. As a result, the search for models capable of ensuring the representation and management of highly heterogeneous data is a hot topic in the data science literature. In this thesis, we aim to propose a solution to address this issue. In particular, we believe that graphs, and more specifically complex networks, as well as the concepts and approaches associated with them, can represent a solution to the problem mentioned above. In fact, we believe that they can be a unique and unifying model to uniformly represent and handle extremely heterogeneous data. Based on this premise, we show how the same concepts and/or approach has the potential to address different open issues in different contexts. ​INGEGNERIA DELL'INFORMAZIONEopenVirgili, Luc