Industry 4.0 and the increasing use of artificial intelligence and machine learning have allowed the analysis of large amounts of data and improve performance across many businesses and sectors. These sectors have significantly increased their reliance on data when making decisions. This paper examines the use of data-driven decision-making on road maintenance planning in Sweden. Data related to road maintenance following the Swedish Road Maintenance Standard, were collected focusing on the International Roughness Index (IRI) and rut depth as primary features. Analyzing such data enabled the identification of maintenance needs within three separate timeframes: immediate, the next five years, and long-term. The model predicted maintenance needs based on the IRI with up to 96% accuracy. However, the model's accuracy dropped to only 67% when predicting maintenance needs over the next five years. In contrast, the model that predicted maintenance needs based on rut depth demonstrated high accuracy across all three timeframes, achieving up to 92% accuracy. The model demonstrated that modern road condition variables are crucial to prediction. In terms of predictions, 2023 IRI measurements were the most important. Based on our findings, this paper improves data-driven decision-making in Swedish road maintenance, resulting in more effective resource allocation and decreased emergency maintenance expenses. Moreover, the study highlights the value of collecting and utilizing more accurate and thorough road state data to enhance these models.

There is currently a huge amount of data being collected about movements of objects. Such data is called spatiotemporal data and paths left by moving-objects are called trajectories. Recently, researchers have been targeting those trajectories for extracting interesting and useful knowledge by means of pattern analysis and data mining. But, it is difficult to analyse huge datasets of trajectories without summarizing them and visualizing them for the knowledge seeker and for the decision makers. Therefore, this research paper focuses on utilizing visual techniques and data mining analysis of trajectory patterns in order to help extract patterns and knowledge in an interactive approach. The research study proposes a research framework which integrates multiple data analysis and visualization techniques in a coherent architecture in support of interactive trajectory pattern visualization for the decision makers. An application case-study of the techniques is conducted on an airport's baggage movement data within the Baggage Handling System (BHS). The results indicate the feasibility of the approach and its methods in visually analysing trajectory patterns in an interactive approach which can support the decision maker. © 2014 Springer International Publishing Switzerland.

Purpose - The aim of this paper is to investigate corporate financial disclosure via Twitter among the top listed 350 companies in the UK as well as identify the determinants of the extent of social media usage to disclose financial information.

Design/methodology/approach – This study applies an unsupervised machine learning technique, namely, Latent Dirichlet Allocation (LDA) topic modeling to identify financial disclosure tweets. Panel, Logistic, and Generalized Linear Model Regressions are also run to identify the determinants of financial disclosure on Twitter focusing mainly on board characteristics.

Findings – Topic modeling results reveal that companies mainly tweet about 12 topics, including financial disclosure, which has a probability of occurrence of about 7 percent. Several board characteristics are found to be associated with the extent of Twitter usage as a financial disclosure platform, among which are board independence, gender diversity, and board tenure.

Originality/value – Extensive literature examines disclosure via traditional media and its determinants, yet this paper extends the literature by investigating the relatively new disclosure channel of social media. This study is among the first to utilize machine learning, instead of manual coding techniques, to automatically unveil the tweets’ topics and reveal financial disclosure tweets. It is also among the first to investigate the relationships between several board characteristics and financial disclosure on Twitter; providing a distinction between the roles of executive versus non-executive directors relating to disclosure decisions.

Ongoing advancements in technology have dramatically changed the disclosure media that companies adopt. Such disclosure media have evolved from traditional paper-based ones to the internet as the new platform for disclosing information on companies’ designated websites; however, the new media for disclosures are currently social media platforms. Among the important disclosures that companies make are Corporate Social Responsibility (CSR) disclosures. These are now deemed indispensable due to growing social awareness among societies. While a significant body of the literature is dedicated to studying CSR disclosures and their determinants on the traditional media, studies examining CSR disclosures on social media are quite few. Hence, the aim of this paper is to investigate corporate social media accounts for CSR disclosures, as well as identify their determinants. The sample of the study is comprised of tweets posted on the Twitter accounts belonging to the FTSE 350 constituents. Topic modeling, an unsupervised machine learning approach, is applied to identify CSR disclosure tweets and regressions are run to identify the determinants of CSR disclosure on Twitter. The utilization of topic modeling to reveal CSR content on social media is considered a new approach to mainstream CSR disclosure literature; as such, this paper examines the new phenomenon of CSR disclosures on social media using new research methods. Results show that the popularity of Twitter as a CSR disclosure platform has risen significantly over the past few years. The paper also provides evidence that the presence of women on company boards, especially non-executive women, is positively associated with the extent of CSR disclosure on Twitter. Results highlight the importance of board independence for CSR disclosure in a social media disclosure context, complementing other disclosure platforms. Larger firms are also found to disclose more CSR information on Twitter compared to smaller ones indicating that firm size plays an important role determining a company’s level of CSR disclosure on social media.

Ongoing advancements in technology have changed dramatically the disclosure media that companies adopt. Such disclosure media have evolved from the traditional paper-based ones, to the internet as the new platform to disclose information via companies’ designated websites. However, currently the new media for disclosures are the social media. The aim of this paper is to investigate corporate social media accounts for financial disclosure, as well as, identify its determinants. The sample of the study is comprised of the tweets posted on the Twitter accounts belonging to the FTSE 350 constituents. Topic modeling is applied to identify financial disclosure tweets and logistic regression is run to identify the determinants of financial disclosure on Twitter. Results show that companies use Twitter to make corporate disclosures and some board characteristics are found to have a significant relationship with financial disclosure.

Ongoing advancements in technology have changed dramatically the disclosure media that companies adopt. Such disclosure media have evolved from the traditional paper-based ones, to the internet as the new platform to disclose information via companies’ designated websites. However, currently the new media for disclosures are the social media. The aim of this paper is to investigate corporate social media accounts for financial disclosure, as well as, identify its determinants. The sample of the study is comprised of the tweets posted on the Twitter accounts belonging to the FTSE 350 constituents. Topic modeling is applied to identify financial disclosure tweets and logistic regression is run to identify the determinants of financial disclosure on Twitter. Results show that companies use Twitter to make corporate disclosures and some board characteristics are found to have a significant relationship with financial disclosure. 

Outliers, the odd objects in the dataset, can be viewed from two different perspectives; the outliers as undesirable objects that should be treated or deleted in the data preparation step of the data mining process, and the outliers as interesting objects that are identified for their own interest in the data mining step of the mining process. In the latter case, outliers shouldn't be removed, that's why one of the main categories of tasks performed by data mining techniques is outlier detection. Applications that make use of such detection include credit card fraud detection and network intrusion detection. Most of the available outlier detection techniques rely in a distance measure to compare the objects in the dataset which imposed the restriction of dealing with numeric data. In this paper a new multivariate similarity-based conformity measure (MSCM) is suggested to be used to detect outliers in datasets that contain attributes of different data types. The MSCM satisfies two other desirable features; being a multivariate measure and giving ranking instead of a binary judgment of the object. The measure has been applied on three different datasets in order to be evaluated; the measure has shown good results in these experiments.

Bookkeeping data free of fraud and errors is a cornerstone of legitimate business operations. Highly complex and laborious financial auditors’ work calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques, nowadays, are being successfully applied to detect fraud and anomalies in data. In accounting, it is a long-established problem to detect financial misstatements deemed anomalies in General Ledger (GL) data. Currently, widely used techniques such as random sampling and manual assessment of bookkeeping rules become challenging and unreliable due to increasing data volumes and unknown fraudulent patterns. To address the sampling risk and financial audit inefficiency, we applied seven supervised ML techniques inclusive of Deep Learning and two unsupervised ML techniques such as Isolation Forest and Autoencoders. We trained and evaluated our models on a real-life GL dataset and used data vectorization to resolve journal entry size variability. The evaluation results showed that the best trained supervised and unsupervised models have high potential in detecting predefined anomaly types as well as in efficiently sampling data to discern higher-risk journal entries. Based on our findings, we discussed possible practical implications of the resulting solutions in the accounting context.

Purpose: This study aims to investigate the extent and characteristics of corporate internet disclosure via companies’ websites as well via social media and networks sites in the four leading English-speaking stock markets, namely, Australia, Canada, the UK and the USA.

Design/methodology/approach: A disclosure index comprising a set of items that encompasses two facets of online disclosure, namely, company websites and social media sites, is used. This paper adopts a data science approach to investigate corporate internet disclosure practices among top listed firms in Australia, Canada, the UK and the USA.

Findings: The results reveal the underlying relations between the determining factors of corporate disclosure, i.e. profitability, leverage, liquidity and firm size. Profitability in its own has no great effect on the degree of corporate internet disclosure whether via company websites or social media sites. Liquidity has an impact on the degree of disclosure. Firm size and leverage appear to be the most important factors driving better disclosure via social media. American companies tend to be on the cutting edge of technology when it comes to corporate disclosure.

Practical implications: This paper provides new insights into corporate internet disclosure that will benefit all stakeholders with an interest in corporate reporting. Social media is an influential means of communication that can enable corporate office to get instant feedback enhancing their decision-making process.

Originality/value: To the best of the authors’ knowledge, this study is amongst few studies of corporate disclosure via social media platforms. This study has adopted disclosure index incorporating social media as well as applying data science approach in disclosure in an attempt to unfold how accounting could benefit from data science techniques.

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network data. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains. © 2014 Springer International Publishing Switzerland.

Information is a key success factor influencing the performance of decision makers, specifically the quality of their decisions. Nowadays, sheer amounts of data are available for organizations to analyze. Data is considered the raw material of the 21^st century, and abundance is assumed with today's 15 billion devices [aka Things!] already connected to the Internet. Accordingly, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such rapidly changing data of high volume, velocity, variety, veracity, and value by using big data analytics. This paper aims to research how big data analytics can be integrated into the decision making process. Accordingly, using a design science methodology, the “Big – Data, Analytics, and Decisions” (B-DAD) framework was developed in order to map big data tools, architectures, and analytics to the different decision making phases. The ultimate objective and contribution of the framework is using big data analytics to enhance and support decision making in organizations, by integrating big data analytics into the decision making process. Consequently, an experiment in the retail industry was administered to test the framework. Accordingly, results showed added value when integrating big data analytics into the decision making process.

This paper addresses a need for developing ex-post evaluation for data-driven decisions resulting in collaboration between humans and machines. As a first step of a design science project, we propose four design objectives for an ex-post evaluation solution, from the perspectives of both theory (concepts from the literature) and practice (through a case of industrial production planning): (1) incorporate multi-faceted decision evaluation criteria across the levels of environment, organization, and decision itself and (2) acknowledge temporal requirements of the decision contexts at hand, (3) define applicable mode(s) of collaboration between humans and machines to pursue collaborative rationality, and (4) enable a (potentially automated) feedback loop for learning from the (discrete or continuous) evaluations of past decisions. The design objectives contribute by supporting development of solutions for the observed lack of ex-post methods for evaluating data-driven decisions to enhance human-machine collaboration in decision making. Our future research involves design and implementation efforts through on-going industry-academia cooperation.

Decisions continue to be an essential topic of utmost importance in every research field and era. However, while decision research has extensively offered a wide range of theories, it remains delved in the past, and needs robustness to sustain the future of data-driven decision-making, encompassing topics and technologies such as big data, analytics, machine learning, and automated decisions. Nowadays, decision processes have evolved, the role of humans as decision makers has changed and become inevitably intertwined with the support of machines, rationalities are no longer limited in the same way, data has become an abundant commodity, and the optimizing of decisions is not so far-fetched a tale as it once was in classical times. Accordingly, there is a dire need for new theories to support new phenomena. This paper aims to propose a modern data-driven decision theory, DECAS, to support the new elements of today’s decisions. Our theory extends upon classical decision theory by proposing three main claims: the (big) data and analytics should be considered as separate elements along with the decision-making process, the decision maker, and the decision; the appropriate collaboration between the decision maker and the analytics (machine) can result in a “collaborative rationality,” extending beyond the bounded rationality which decision makers were classically characterized by; and finally, the proper integration of the five elements, and the correct selection of data and analytics, can lead to more informed, and possibly better, decisions.  Hence, the theory is elaborated in the paper, and introduced to some data-driven decision examples.

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions.

Human-machine collaboration has potentially led to higher quality and more informed data-driven decisions. However, evaluating these decisions is necessary to measure the benefits, as well as enable experiential learning and posterior rationalization of the results and consequences. Nevertheless, the multiplicity of human-machine collaboration modes, as well as the multi-faceted nature of data-driven decisions complicates evaluation, and evaluation solutions are lacking both in research and in practice. This is further reflected in the complexity of incorporating evaluation in the design of such data-driven decision making systems, since developers are left without theoretically grounded and practically feasible principles to guide implementation. In this paper, we propose a set of five design principles, explicated from theory and practice, for systems implementing data-driven decision evaluation as the output of design science research cycles. The design principles are: 1) multi-faceted evaluation criteria, 2) unified viewpoint, 3) collaborative rationality, 4) processual ex-post evaluation, and 5) adaptive feedback and learning loops. They are further contextualized in the case of AI-enabled menu design at Antell, an innovative pioneer in the restaurant business in Finland, and consequently evaluated by the development managers of the project. Accordingly, the design principles contribute to the knowledge base on metahuman systems and data-driven decision evaluation, by concretizing existing normative concepts into prescriptive knowledge, also guiding future research and generalizing towards a design theory. Furthermore, they provide implementable statements for designing and developing such systems in practice and can be used as a checklist to compare and evaluate existing systems.

The world is witnessing an unprecedented interest in big data. Big data is data that is big in size (volume), big in variety (structured; semi-structured; unstructured), and big in speed of change (velocity). It was reported that almost 90% of the data worldwide was just created in the past 2 years. Therefore, this paper is an attempt to align ERP systems with big data. The objective is to suggest a future research agenda to bring together big data and ERP. While almost everyone is talking about big data at the product or tool level, relationship with social media, relationship with Internet of things, etc. no one has tried to integrate big data and ERP. A research agenda is discussed and introduced in this paper.

Classification of enterprise portal systems (EP) based on their features is the topic of this paper. We propose a classification based on cluster analysis which depends on features collected from the internet. Results showed that systems were found to belong to different classes based on their features

There is currently plenty of research concerning the effect of Enterprise Resource Planning (ERP) on business performance. Previous research has shown a mixed relationship between ERP and business performance where some suggested that ERP improves performance while others found that it does not. This paper investigates this topic by analyzing a detailed case-study consisting of an Egyptian SME, as a branch of a multinational company. The results indicate in general many benefits in business performance were achieved after implementing the ERP as reported by many users, but have also shown that few benefits previously linked to ERP were not fully achieved.

 Since the inception of civilization, humans have monopolized decision-making. Although human intelligence is capable of producing excellent decisions, our history is littered with cases of disastrous choices. In general, bad decisions result from many factors, including cognitive biases. It is important to avoid bad decisions at personal, organizational and societal levels, but are they avoidable? Artificial Intelligence (AI) has been upheld as the remedy for our biases. This opinion paper investigates if such a claim is warranted. We briefly review the literature on cognitive biases, AI biases, and hybrid intelligence. We conclude by suggesting that although AI may have tremendous benefits for decision-making, in its present form, it cannot eliminate all of our human biases, and can contrarily result in additional biases. More research on the use of AI in decision-making is needed. 

In an ideal world, the way we learn should align with the way we are taught. However, this is not always the case, and we need to rethink our approach to teaching in academic institutions such as universities to help students learn at their own pace, where innovative technology plays a vital role in accomplishing this goal. This study proposes the use of artificial intelligence (AI) as a means of teaching in academic institutions. Specifically, an AI-powered conversational bot called conversational AI bot for efficient learning (COFFEE) is designed, implemented, and will used as an adaptive learning technology. COFFEE will analyze the students’ learning habits and adjust the teaching style based on their strengths and weaknesses. Teaching and learning are closely linked to the disciplinary areas of education, behavior, and technology. By combining these areas into a conversational bot equipped with AI algorithms and access to big data, we can disrupt traditional teaching methods. This will benefit students who require special attention, such as those with severe learning disabilities who may not receive the necessary attention from instructors due to time constraints or lack of experience. COFFEE is a valuable tool that can be used in academic institutions at all levels, including primary and secondary education. It will be based on the latest advancements in AI and data science and will be designed to be feasible, reliable, and customizable.

Many technologies have been used in education. Such technologies fall under three main types: learning management systems; education data mining; and AI-enabled technologies. This report focuses on the use of robotics in interactive education. Over the past few years, interest in utilization of robotics in education has increased. Multiple attempts have been made, globally, in order to introduce robotics in education. Our report reveals that robotics have been used in education either in front scene acting as a teacher or in back scene supporting the teaching process. Our report also reveals that robotics are able to address unsolved educational issues such as achievement gaps and teachers gaps, in addition to the assistance it provides in some specific use cases. Further research efforts are indeed required to fully understand the exact role, current and future, or robotics in education. The report also introduce some challenges in using robotics in education e.g., Communication breakdowns, navigation capabilities, and the feeling of remote students

This study proposes a model to assess data-driven decision-making (DDDM) readiness in organizations. We present the results from investigating the DDDM readiness of a Swedish organization in the food industry. We designed and developed a questionnaire to collect data about the organization’s decision-making and IT systems. We conducted eleven interviews at the case study organization: ten with various functional decision-makers and one with the IT Manager about IT systems. The interview data were then analyzed against known decision theories and state-of-the-art DDDM. Based on the interview outcomes, we analyze the data according to the assessment model and recommend changes to the organization’s readiness for data-driven decisions. The findings show that while the organization was assessed as ready in the decision-making process and decision-maker pillars, it was not ready in the data or analytics pillars. Accordingly, we recommend a set of actions, including considering integration and decision systems, further developing dashboards, increasing data and analytics resources (such as enterprise data warehouse, big data management tools, data lake environment, and data analytics algorithms), and defining key roles necessary for digitalization and DDDM (such as Data Engineer, Data Scientist, Business Intelligence Specialist, Chief Data Officer, and Data Warehouse Designer/Administrator). The contribution of this study is the DDDM readiness assessment model, accompanied by a questionnaire for determining the readiness level in organizations.

This literature review paper summarizes the state-of-the-art research on big data analytics. Due to massive amount of data exchanged everyday and the increased need for better data-based decision, businesses nowadays are looking for ways to efficiently manage, and optimize these huge datasets. Moreover, because of globalization, partnerships, value networks, emergence of social networks, and the huge information flow across and within enterprises, more and more businesses are interested in utilizing big data analytics. The main focus of this paper is to elucidate knowledge on the characteristics of big data analytics literature as well as explore the areas that lack sufficient research within the big data analytics domain, suggest future research avenues, as well as, present the current research findings that could aid practitioners, researchers, and vendors when embarking on big data analytics projects. Towards that end, we have reviewed 24 publications between 2010 and 2014. Results of text mining the papers revealed that they belong to three clusters with both common as well as distinct characteristics. The reviewed papers were clustered into three main themes, 1) technical algorithsms; 2) processing, cloud computing, opportunities & challenges; and 3) performance, prediction, and distributed systems.

Given the different types of artifacts and their various evaluation methods, one of the main challenges faced by researchers in design science research (DSR) is choosing suitable and efficient methods during the artifact evaluation phase. With the emergence of big data analytics, data scientists conducting DSR are also challenged with identifying suitable evaluation mechanisms for their data products. Hence, this conceptual research paper is set out to address the following questions. Does big data analytics impact how evaluation in DSR is conducted? If so, does it lead to a new type of evaluation or a new genre of DSR? We conclude by arguing that big data analytics should influence how evaluation is conducted, but it does not lead to the creation of a new genre of design research.

This chapter is an effort towards illustrating the use of expert panels (EP) as a means of eliciting knowledge from a group of enterprise resource planning (ERP) experts in an exploratory research. The development of a cost estimation model for ERP adoptions is very crucial for research and practice, and that was the main reason behind the willingness of experts to participate in this research. The use of EP was very beneficial as it involved various data collection and visualization techniques, as well as data validation and confirmation. Arguments for using EP over other group techniques are presented in this chapter. Experts modified and enhanced the initial cost drivers list and their sub-factors significantly, as they added, modified, merged and split different costs. Moreover, they ranked the cost drivers according to their weight on total costs. All of this helped the authors to better understand relationships among various cost factors

This paper is an effort towards illustrating the use of expert panel (EP) as a mean of eliciting knowledge from a group of enterprise resource planning (ERP) experts as an exploratory research. The development of a cost estimation model (CEM) for ERP adoptions is very crucial for research and practice, and that was the main reason behind the willingness of experts to participate in this research. The use of EP was very beneficial as it involved various data collection and visualisation techniques, as well as data validation and confirmation. Beside its advantages, one of the main motives for using a group technique is that it is difficult to find a representative sample for a casual survey method, as ERP experts and consultants are rare to find, especially in the scope of SMEs' ERP implementations. It is worth noting that the panel reached consensus regarding the results of the EP. The experts modified and enhanced the initial cost drivers (CD) list largely, as they added, modified, merged and split different costs drivers. In addition, the experts added CF (sub-factors) that could influence or affect each cost driver. Moreover, they ranked the CD according to their weight on total costs. All of this helped the authors to better understand relationships among various CF

An analytics-empowered enterprise system looks to many organizations to be a far-fetched target, owing to the vast amounts of factors that need to be controlled across the implementation lifecycle activities, especially during usage and maintenance phases. On the other hand, advanced analytics techniques such as machine learning and data mining have been strongly present in academic as well as industrial arenas through robust classification and prediction. Correspondingly, this paper is set out to address a methodological approach that works on tackling post-live implementation activities, focusing on employing advanced analytics techniques to detect (business process) problems, find and recommend a solution to them, and confirm the solution. The objective is to make enterprise systems self-moderated by reducing the reliance on vendor support. The paper will profile an advanced analytics engine architecture fitted on top of an enterprise system to demonstrate the approach. Employing an advanced analytics engine has the potential to support post-implementation activities. Our research is innovative in two ways: (1) it enables enterprise systems to become self-moderated and increase their availability; and (2) the IT artifact i.e., the analytics engine, has the potential to solve other problems and be used by other systems, e.g., HRIS. This paper is beneficial to businesses implementing enterprise systems. It highlights how enterprise systems could be safeguarded from retirement caused by post-implementation problems.

Most scientists are accustomed to make predictions based on consolidated and accepted theories pertaining to the domain of prediction. However, nowadays big data analytics (BDA) is able to deliver predictions based on executing a sequence of data processing while seemingly abstaining from being theoretically informed about the subject matter. This paper discusses how to deal with the shift from theory-driven to process-driven prediction through analyzing the BDA steps and identifying the epistemological challenges and various needs of theoretically informing BDA throughout data acquisition, preprocessing, analysis, and interpretation. We suggest a theory-driven guidance for the BDA process including acquisition, pre-processing, analytics and interpretation. That is, we propose—in association with these BDA process steps—a lightweight theory-driven approach in order to safeguard the analytics process from epistemological pitfalls. This study may serve as a guideline for researchers and practitioners to consider while conducting future big data analytics.

This paper provides a framework for comparison between the implementation of ERP systems in-house versus the in-cloud implementations. The paper first establishes a framework for the comparison based on three factors: pre-live i.e., the implementation methodologies of both options, post-live i.e., cost, time, and the user-friendliness of the systems, and third is other factors i.e., security and scalability. Results show that in-cloud systems are faster to implement, less costly and easier to use, and they are scalable. Inhouse system, compare to in-cloud, give organizations more control and hence they seems to be more secure to many organizations

In the public sector, the EU legislation requires preservation and opening of increasing amounts of heterogeneous digital information that should be utilized by citizens and businesses. While technologies such as big data analytics (BDA) have emerged, opening of digital archives and collections at a large scale is in its infancy. Opening archives and collections involve also particular requirements for recognizing and managing issues of privacy and digital rights. As well, ensuring the sustainability of the opened materials and economical appraisal of digital materials for preservation require robust digital preservation practices. We need to proceed beyond the state-of-the-art in opening digital archives and collections through the means of emerging big data analytics and validating a novel concept for analytics which then enables delivering of knowledge for citizens and the society. We set out an agenda for using BDA as our strategy for research and enquiry and for demonstrating the benefit of BDA for opening digital archives by civil servants and for citizens. That will –eventually -transform the preservation practices, and delivery and use opportunities of public digital archives. Our research agenda suggests a framework integrating four domains of inquiry, analytics-enhanced appraisal, analytics-prepared preservation, analytics-enhanced opening, and analytics-enhanced use, for utilizing the BDA technologies in the domain of digital archives and collections. The suggested framework and research agenda identifies initially particular BDA technologies to be utilized in each of the four domains, and contributes by highlighting a need for an integrated “public understanding of big data” in the domain of digital preservation.

Huge amount of location and tracking data is gathered by location and tracking technologies, such as global positioning system (GPS) and global system for mobile communication (GSM) devices; leading to the collection of large spatiotemporal datasets and to the opportunity of discovering usable knowledge about movement behavior. Movement behavior can be extremely useful in many ways when applied, for example, in the domain of traffic management, planning metropolitan areas, mobile marketing, tourism, etc. In this research, we move towards this direction and propose a framework for finding trajectory patterns of frequent behaviors using GSM data. The research question is «how to use trajectory data analysis in support of solving traffic management problems utilizing data mining techniques?» Our framework is illustrated to explain how GSM data can provide accurate information about population movement behavior, and hence support traffic decisions. © 2014 Springer International Publishing Switzerland.

This research recognizes the pressing need for innovative research in healthcare, enabling the transition towards analytics, by explaining how previous studies utilized big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to understand the literature, identify research gaps, and posit research questions for researchers, academic institutions, and governmental healthcare organizations. We intend to explain how contemporary analytics have been used to address healthcare concerns as well as to posit several research questions for future studies based on gaps which we have identified. The study has multi-folds contribution areas: first, it provides a state-of-the-art review to healthcare analytics, second, it posits a research agenda to advance the knowledge in this area further.

This research addresses the demanding need for research in healthcare analytics, by explaining how previous studies have used big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to examine the literature, identify research gaps, and propose a research agenda for researchers, academic institutions, and governmental healthcare organizations. The study contributes to the body of literature by providing a state-of-the-art review of healthcare analytics as well as proposing a research agenda to advance the knowledge in this area. The results of this research can be beneficial for both healthcare science and data science researchers as well as practitioners in the field.

The emergence of the Enterprise 2.0 technologies indicates that they can provide value to different types of users and potentially different types of value. Many published research explored what these E2.0 tools and applications can offer to organizations, such as collaboration platforms, social networking and user-created content, enhancing their productivity and management among employees. However, little research was devoted to study the effect these tools and applications have on the decision making process. Decision 2.0 has received little attention in literature, especially from the standpoint of making use of the "crowd". Therefore, this paper focuses on this research gap with a case study in an attempt to elucidate and extract knowledge to answer this question "How does decision 2.0 make use of the crowd to support the traditional decision making process and hence add value to organizations through collaboration and collective intelligence?"

Introducing an Enterprise Resource Planning (ERP) system within an organization can bring many benefits and paybacks, yet an effective implementation of a fully functioning ERP system is still a challenge, the odds are high the costly investment might turn into an implementation failure or even lead to bankruptcy. To prevent such situations, organizations need to go through several changes, and carefully manage the critical success factors affecting each stage of the ERP implementation lifecycle, respectively. Previous studies have observed that the majority of the challenges faced during the implementation of ERP systems arise from social and organizational aspects rather than technical ones. This is where gamification comes to the rescue. This research adopts a design science paradigm in an attempt to develop a gamified process for the ERP lifecycle, to ease and enhance the ERP implementation process. The objective of this research is threefold. Firstly, to explore the benefits ERP systems can render via the gamification of the ERP lifecycle. Secondly, to pinpoint the ERP lifecycle phases that are most likely to benefit from gamification. Thirdly, to gamify these formerly identified phases; that were found to be mostly affected by gamification, and test for the impact of gamification on them.

Enterprise resource planning (ERP) systems adoptions require substantial resources and investments. The majority of businesses around the globe can be considered to be small and medium sized enterprises (SMEs). Thus, SMEs are seen to be typical companies that are the cornerstone of most economies. Compared with large enterprises, an SME-context contains several characteristics, and scarcity of resources is among the top of them. For SMEs, unplanned costs escalation could pose a serious threat to their stability and survival in the market. Frequently, ERP projects have crossed their estimated budgets and schedules. Researchers and practitioners state that a prevailing number of ERP adoption projects fail due to inaccurate or to too optimistic budgets/schedules. In addition, many organizations face difficulties in identifying the potential cost factors that could occur during their ERP adoption lifecycle. While focusing on the SME-context, this research attempts to identify potential costs that could occur in ERP adoptions. The research method employed in this study targeted diverse stakeholders and experts involved in ERP projects in Egypt. This research provides a list of cost factors and their classifications that could aid adopting organizations to better estimate their needed ERP project budgets. In particular, this research explores the direct and indirect cost factors that occur in ERP adoptions in Egyptian SMEs. Also, this study investigates the influence of some SME-specific contextual factors on costs. Moreover, the paper provides a ranking of cost factors according to their impact on total adoption costs.

A lot of research has been undertaken focusing on ERP systems lifecycles, but very little paid attention to retirement. ERP retirement means the replacement of an ERP with another. The aim of this research paper is to investigate why and when should organizations retire their ERP systems. A convenience case study of an SME has been selected from Egypt. The case study under investigation has retired their local ERP system and replaced it with SAP ERP. Results of our analysis indicated that reasons of retirement were: wrong selection, users were not involved in the selection process, and lack of an official implementation methodology. This is considered a new finding since main stream literature was mainly focused on retirement after maturity

In 2011, at the Hanover Fair, the term Industry 4.0 was first coined. In October 2012, the Working Group on Industry 4.0, presented a set of implementation recommendations to the German government. The term Industry 4.0 initiates from a project in the high-tech strategy of the German government. Such project advocates the computerization of the manufacturing industry. It is also known as the 4th industrial revolution. Precisely speaking, industry 4.0 is based on the technological concepts of cyber-physical systems, Internet of Things (IoT), which enables the Factory of the Future (FoF). Within the modular structured smart factories of Industry 4.0, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the IoT, Cyber-physical systems communicate and cooperate with each other and with humans in real time. Enterprise resource planning (ERP) systems are considered the backbone for the Industry 4.0. Thus, this paper attempts to answer the research question: “Are today’s ERP systems ready for the FoF?”. We have conducted interviews with manufacturers, ERP vendors, and partners in order to check on the readiness of ERP systems for the FoF. Our results show that ERP systems are ready for the FoF.

Big data visualization tools are analytical tools used by organizations for the purpose of discovering knowledge. With the support of interactive visual interfaces, methods and techniques for analyzing big data are applied to facilitate the knowledge discovery process and provide domain relevant insights. Many studies, both academic and industrial, have been conducted in order to investigate visualization tools. However, little research has been conducted in order to study how big data visualization tools could be selected. Consequently, this research is setout to fill-in this gap. Accordingly, a three phases (literature review, Delphi method, and exploratory case study) research process is conducted to propose a big data visualization tool best-practice selection model. The use of this model would help organizations in selecting and obtaining the appropriate visualization tool. The results of this research revealed a number of criteria elements, which formulate the best-practice model for big data visualization tool selection. A total number of 36 criteria have been agreed upon by a number of 14 big data experts. Such criteria belong to six main types: technical, visualization, collaboration & mobility, operational, data governance and managerial requirements. An exploratory case study was conducted on a multi-national telecommunication company in order to test the usability of the model, which attained positive feedback results.

Transforming the state-of-the-art definition and anatomy of enterprise systems (ESs) seems to some academics and practitioners as an unavoidable destiny. Value depletion lead by early retirement and/or replacement of ESs solutions has been a constant throughout the past decade. That did drive an enormous amount of research that works on addressing the problems leading to the resource drain. The resource waste had persisted throughout the ESs implementation lifecycle phases and dimensions especially post-live phases; leading to depleting the value of the social and technical dimensions of the lifecycle. Parallel to this research stream, the momentum gained by deep learning (DL) algorithms and platforms has been exponentially growing to fuel the advancements toward artificial intelligence and automated augmentation. Correspondingly, this paper is set out to present five key research directions through which DL would take part as a contributor towards the transformation of the ESs state-of-the-art. The paper reviews the ESs implementation lifecycle challenges and the intersection with DL research conducted on ESs by analyzing and synthesizing key basket journals (list of the Association of Information Systems). The paper also presents results from several experiments showcasing the effectiveness of DL in adding a level of augmentation to ESs by analyzing a large set of data extracted from the Atlassian Jira Software Issue Tracking System across different ecosystems. The paper then concludes by presenting the research directions and discussing socio-technical research courses that work on key frontiers identified within this scholarly work.

This research investigates how companies operating in emerging markets and investing in latest IT solutions can be supported in developing maturity in business-IT alignment faster than their predecessors in developed countries. It follows up the consultant perspective through documenting ex-post the participant’s observations, reflected through participant objectivation, to analyze the activities con-ducted, the obstacles faced and the success reached while trying to mature business-IT alignment in selected companies operating in the Arab World. From the five cases included in the sample, evidence was found that specific consultant’s interceptions can indeed “fast-track” the needed improvement in business-IT alignment, especially when the sense of crisis in the company creates a readiness to em-brace the change and the subsequent benefits are obvious enough to those involved to sustain the newly reached maturity level. However, the personality and behavior of the CEO, the attitude of middle management, and the corporate culture as a whole may obstruct all efforts of “fast-tracking” any time, especially when the actors involved adhere to traits deeply rooted in the region’s hierarchical traditions.

Integrating business intelligence (BI) as a framework to support EGov decisions is vital. Data mining, a major BI component, is a group of techniques used to find hidden patterns and unknown facts in data sets. In this paper, we implemented the Pan America Health Organization (PAHO) and World Health Organization (WHO) PAHO/WHO hospital safety index to a data set containing six educational hospitals from Alexandria, Egypt. The index results show that five hospitals fall in one category and a remaining hospital falls in another category. Based on the results decisions were about to be taken to allocate resources to enhance safety of hospitals. To validate the index results, we used cluster analysis, a data mining technique. Results show that hospitals fall in two classes; class one has three hospitals whereby class two has the remaining three. That is, by introducing one of the data mining techniques to one of the EGov decision areas we were able to gain more knowledge about the problem domain and hence make more informative decisions. The mining results call for more investigative actions to be taken by EGov projects in order to enhance decision quality to help achieving better safety of hospitals.