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

New Trends in Development of Services in the Modern Economy

The services sector strategic development unites a multitude of economic and managerial aspects and is one of the most important problems of economic management. Many researches devoted to this industry study are available. Most of them are performed in the traditional aspect of the voluminous calendar approach to strategic management, characteristic of the national scientific school. Such an approach seems archaic, forming false strategic benchmarks. The services sector is of special scientific interest in this context due to the fact that the social production structure to the services development model attraction in many countries suggests transition to postindustrial economy type where the services sector is a system-supporting sector of the economy. Actively influencing the economy, the services sector in the developed countries dominates in the GDP formation, primary capital accumulation, labor, households final consumption and, finally, citizens comfort of living. However, a clear understanding of the services sector as a hyper-sector permeating all spheres of human activity has not yet been fully developed, although interest in this issue continues to grow among many authors. Target of strategic management of the industry development setting requires substantive content and the services sector target value assessment

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

E-finance-lab at the House of Finance : about us

The financial services industry is believed to be on the verge of a dramatic [r]evolution. A substantial redesign of its value chains aimed at reducing costs, providing more efficient and flexible services and enabling new products and revenue streams is imminent. But there seems to be no clear migration path nor goal which can cast light on the question where the finance industry and its various players will be and should be in a decade from now. The mission of the E-Finance Lab is the development and application of research methodologies in the financial industry that promote and assess how business strategies and structures are shared and supported by strategies and structures of information systems. Important challenges include the design of smart production infrastructures, the development and evaluation of advantageous sourcing strategies and smart selling concepts to enable new revenue streams for financial service providers in the future. Overall, our goal is to contribute methods and views to the realignment of the E-Finance value chain. ..

Assessing factors of behavioral intention to use Big Data Analytics (BDA) in banking and insurance sector: proposition of an integrated model

Banking and insurance sectors have long been largely data-driven by nature. However, with the rise in the predominance of data flooding from several sources resulting from the introduction of new customers and markets, with the help of Big Data Analytics, value can be extracted more effectively, and analysis of this type of unstructured data combined with a wide range of datasets can be used to efficiently and precisely extract commercial value. The aim of this paper is to develop a conceptual framework to explain the intention of information technology practitioners in banks and insurance companies to use Big Data Analytics by exploiting the Technology Acceptance Model (TAM) joined by the Task-Technology-Fit paradigm, information quality, security, trust, and the moderating effect of managerial commitment by top management on the relationship between users’ perception and their intention to use, in order to conceptualize and test an integrated framework for analyzing and measuring attitudes toward the usage of Big Data Analytics. This paper contributes by proposing the model to assess the factors that influence users’ intention towards the use of Big Data Analytics, by asserting users’ perception towards the technology, trust factor, security and the effect of managerial commitment. Although the model we developed in this paper is conceptual and still needs to be tested empirically, it will serve as a basic framework for further research that is designed to evaluate factors affecting IT practitioners’ attitudes towards the adoption of Big Data Analytics within the finance sector.   Keywords: Big Data Analytics, TAM, TTF, Security, Trust, Managerial commitment, Bank, Insurance  JEL Classification: O32 Paper type: Theoretical ResearchLes secteurs de la banque et de l'assurance sont depuis longtemps largement axés sur les données par nature. Cependant, avec l'augmentation de la prédominance de l'inondation de données provenant de plusieurs sources résultant de l'introduction de nouveaux clients et marchés, avec l'aide du Big Data Analytics, la valeur peut être obtenue plus efficacement, et l'analyse de ce type de données non structurées combinées à un large éventail d'ensembles de données peut être utilisée pour extraire efficacement et précisément la valeur commerciale. L'objectif de cet article est de développer un cadre conceptuel pour expliquer l'intention des praticiens des technologies de l'information dans les banques et les compagnies d'assurance d'utiliser le Big Data Analytics en exploitant le Modèle d'Acceptation de la Technologie (TAM) associé au paradigme Adéquation Tache-Technologie, la qualité de l'information, la sécurité, la confiance et l'effet modérateur de l'engagement du management sur la relation entre la perception des utilisateurs et leur intention d'utilisation, afin de conceptualiser et de tester un cadre intégré pour analyser et mesurer les attitudes envers l'utilisation du Big Data Analytics. Cet article contribue en proposant un modèle pour évaluer les facteurs qui influencent l'intention des utilisateurs vers l'utilisation du Big Data Analytics, en affirmant la perception des utilisateurs envers la technologie, le facteur de confiance, la sécurité et l'effet de l'engagement managérial. Bien que le modèle que nous avons développé dans cet article soit conceptuel et nécessite encore d'être testé empiriquement, il servira de cadre de base pour des recherches ultérieures conçues pour évaluer les facteurs affectant les attitudes des informaticiens envers l'adoption du Big Data Analytics dans le secteur financier.   Keywords: Big Data Analytics, TAM, TTF, Security, Trust, Managerial commitment, Bank, Insurance  JEL Classification: O32 Paper type: Theoretical Researc

An Industry-Specific Investigation on Artificial Intelligence Adoption: The Cases of Financial Services and Manufacturing

Artificial Intelligence (AI) has a lasting transformational effect on industries worldwide. Former research has primarily focused on AI adoption as a business phenomenon without considering different industries. Those are characterized by unique attributes that may influence how modern technologies are implemented. In order to initiate non-generalized research in that field, industry-specific drivers and barriers to firm-level AI adoption in the financial services and the manufacturing industry are analyzed. Drawing on the Technology-Organization-Environment (TOE) framework, it was possible to paint a holistic picture of use cases and unique, but also general drivers and barriers of AI adoption for each industry. Ultimately, by bringing these two viewpoints together, a theory of hard (generalizable) and soft (industry-specific) AI adoption factors was developed. Therefore, the findings serve as a basis for further industry-specific research and provide business stakeholders and executives with a transparent handbook about industry insights and AI knowledge

Needs of Service Identification for Service-Oriented Business Process Management

Since the trend of adopting SOA into enterprise applications, the needs for definition and identification of services have been recognized. There is a growing body of research carried out on the identification of different types of services. Identifying right granularity services is important: if the service is of large size, it goes against the reusability principle of SOA, whereas if the service is of small size, then it causes unnecessary computing power for implementing any business functions. Without a formal (semi)- automatic approach to identify services, it is difficult in migrating existing systems into service-oriented systems. This paper explores the need for service identification for service-oriented business process management systems. The current approaches, techniques and methods of service identification are reviewed, and limitations of the each approach is analysed. New requirements and techniques are demonstrated in creating improved dynamic services with consideration for interoperability, modularity, reusability within information environment

Elucidation of big data analytics in banking : a four-stage Delphi study

Purpose In today's networked business environment, a huge amount of data is being generated and processed in different industries, which banking is amongst the most important ones. The aim of this study is to understand and prioritize strategic applications, main drivers, and key challenges of implementing big data analytics in banks. Design/methodology/approach To take advantage of experts' viewpoints, the authors designed and implemented a four-round Delphi study. Totally, 25 eligible experts have contributed to this survey in collecting and analyzing the data. Findings The results revealed that the most important applications of big data in banks are “fraud detection” and “credit risk analysis.” The main drivers to start big data endeavors are “decision-making enhancement” and “new product/service development,” and finally the focal challenge threatening the efforts and expected outputs is “information silos and unintegrated data.” Originality/value In addition to stepping forward in the literature, the findings advance our understanding of the main managerial issues of big data in a dynamic business environment, by proposing effective further actions for both scholars and decision-makers

Navigating Secure Banking IT Landscapes: Insights for Solution Architects and Technical Leaders

In "Navigating Secure Banking IT Landscapes: Insights for Solution Architects and Technical Leaders," the authors examine the evolving strategies and intricate problems associated with banking IT infrastructure security. The purpose of this research is to offer technical professionals and solution architects useful information about the critical need for better cybersecurity measures. Examining new technology, industry standards, and innovative approaches tailored to the banking IT landscape, the study integrates theoretical frameworks with practical implications. Abstract: The study aims to empower banking sector leaders to make informed decisions, enhance technological foundations, and proactively navigate the ever-changing terrain of safe banking IT and persistent cyber threats. Research concludes that proactive incident response planning, frequent audits and continual monitoring are steps that IT executives may do to guarantee the long-term financial viability of the banking business. The auditor performed a thorough job of detecting cybersecurity occurrences, differentiating between genuine and fraudulent payment gateways, and determining the false positive rate ratio by applying networking theory