Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining

INTEGRATING KANO MODEL WITH DATA MINING TECHNIQUES TO ENHANCE CUSTOMER SATISFACTION

The business world is becoming more competitive from time to time; therefore, businesses are forced to improve their strategies in every single aspect. So, determining the elements that contribute to the clients\u27 contentment is one of the critical needs of businesses to develop successful products in the market. The Kano model is one of the models that help determine which features must be included in a product or service to improve customer satisfaction. The model focuses on highlighting the most relevant attributes of a product or service along with customers’ estimation of how these attributes can be used to predict satisfaction with specific services or products. This research aims at developing a method to integrate the Kano model and data mining approaches to select relevant attributes that drive customer satisfaction, with a specific focus on higher education. The significant contribution of this research is to improve the quality of United Arab Emirates University academic support and development services provided to their students by solving the problem of selecting features that are not methodically correlated to customer satisfaction, which could reduce the risk of investing in features that could ultimately be irrelevant to enhancing customer satisfaction. Questionnaire data were collected from 646 students from United Arab Emirates University. The experiment suggests that Extreme Gradient Boosting Regression can produce the best results for this kind of problem. Based on the integration of the Kano model and the feature selection method, the number of features used to predict customer satisfaction is minimized to four features. It was found that either Chi-Square or Analysis of Variance (ANOVA) features selection model’s integration with the Kano model giving higher values of Pearson correlation coefficient and R2. Moreover, the prediction was made using union features between the Kano model\u27s most important features and the most frequent features among 8 clusters. It shows high-performance results

ERP implementation methodologies and frameworks: a literature review

Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

Decision-making in Software Evaluation: like to, want to and have to

This paper presents findings from three participative case studies in the selection of a Remote Assistance application. The needs for a Remote Assistance application were different: vanity project, commercial pressure, COVID-19 imposed travel bans. The case organisations had different motivations, different evaluation approaches and different decision flows. However, none of the organisations followed the formally described approaches of criteria definition, criteria ranking, score calculation and decision. The studies show chaotic and iterative processes which are influenced by participants’ attitudes and humours more than by formal procedures and business-school teachings. The motivations for IT-use appear to influence the decisions more than the (in-)formality of the evaluation process. The paper concludes with a discussion of the findings and proposals for further research

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018

Managing cyber risk in organizations and supply chains

In the Industry 4.0, modern organizations are characterized by an extensive digitalization and use of Information Technology (IT). Even though there are significant advantages in such a technological progress, a noteworthy drawback is represented by cyber risks, whose occurrence dramatically increased over the last years. The information technology literature has shown great interested toward the topic, identifying mainly technical solutions to face these emerging risks. Nonetheless, cyber risks cause business disruption and damages to tangible and intangible corporate assets and require a major integration between technical solutions and a strategic management. Recently, the risk management domain and the supply chain literature have provided studies about how an effective cyber risk management process should be planned, to improve organizational resilience and to prevent financial drawbacks. However, the aforementioned studies are mainly theoretical and there is still a significant lack of empirical studies in the management literature, measuring the potential effects of cyber threats within single companies, and along networks of relationships, in a wider supply chain perspective. The present thesis aims at filling some of these gaps through three empirical essays. The first study has implemented a Grounded Theory approach to develop an interview targeting 15 European organizations. Afterwards, the fuzzy set Qualitative Comparative Analysis (fsQCA) has been performed, in order to ascertain how managers perceive cyber risks. Results contradict studies that focus merely on technical solution, and con\ufb01rm the dynamic capability literature, which highlights the relevance of a major integration among relational, organizational, and technical capabilities when dealing with technological issues. Moreover, the study proposes a managerial framework that draws on the dynamic capabilities view, in order to consider the complexity and dynamism of IT and cyber risks. The framework proposes to implement both technical (e.g. software, insurance, investments in IT assets) and organizational (e.g. team work, human IT resources) capabilities to protect the capability of the company to create value. The second essay extends the investigation of the drawbacks of cyber risks to supply chains. The study conducts a Grounded Theory empirical investigation toward several European organizations that rely on security and risk management standards in order to choose the drivers of systematic IT and cyber risk management (risk assessment, risk prevention, risk mitigation, risk compliance, and risk governance). The evidence gleaned from the interviews have highlighted that investments in supply chain mitigation strategies are scant, resulting in supply chains that perform like they had much higher risk appetite than managers declared. Moreover, it has emerged a general lack of awareness regarding the effects that IT and cyber risks may have on supply operations and relationships. Thus, a framework drawing on the supply chain risk management is proposed, offering a holistic risk management process, in which strategies, processes, technologies, and human resources should be aligned in coherence with the governance of each organization and of the supply chain as a whole. The \ufb01nal result should be a supply chain where the actors share more information throughout the whole process, which guarantees strategic bene\ufb01ts, reputation protection, and business continuity. The third essay draws on the Situational Crisis Communication Theory (SCCT) to ascertain whether and how different types of cyber breaches differently affect the corporate reputation, defined as a multidimensional construct in which perceptions of customers, suppliers, (potential) employees, investors and local communities converge. Data breaches have been categorized into three groups by the literature, meaning intentional and internal to the organization (e.g., malicious employees stealing customers\u2019 data), unintentional and internal to the organization (e.g., incorrect security settings that expose private information), and intentional and external to the organization (e.g., ransomware infecting companies\u2019 software). However, this is among the first study to analyse the different reputational drawbacks these types may cause. Moreover, the study considers that, in the industry 4.0 era, social media analysis may be of paramount importance for organizations to understand the market. In fact, user-generated content (UGC), meaning the content created by users, might help in understanding which dimensions of the corporate have been more attacked after a data breach. In this context, the study implements the Latent Dirichlet Allocation (LDA) automated method, a base model in the family of \u201ctopic models\u201d, to extract the reputational dimensions expressed in UGC of a sample of 35 organizations in nine industries that had a data breach incident between 2013 and 2016. The results reveal that in general, after a data breach, three dimensions\u2014perceived quality, customer orientation and corporate performance\u2014 are a subject of debate for users. However, if the data breach was intentional ad malicious, users focused more on the role of firms\u2019 human resources management, whereas if users did not identify a responsible, users focused more on privacy drawbacks. The study complements crisis communication research by categorizing, in a data breach context, stakeholders\u2019 perceptions of a crisis. In addition, the research is informative for risk management literature and reputation research, analysing corporate reputation dimensions in a data breach crisis setting

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse