Towards the adoption of technological innovations: decision processes in transport policy definition

The widespread of technological innovations is rapidly changing the way modern societies are organized. Such phenomenon highly affects the economy of most developed countries (and, more recently, of developing countries, too), influencing work organization and habits. Besides, technological innovations modify the way in which transport systems are organized, by introducing new transport solutions as well as by upgrading the performances of the existing transport systems, in accordance to a more efficient organization. Several tools have been designed to predict the effects of the adoption of technological innovations in transport. The aim of this paper is to deal with the decision processes involved in the definition of the transport policies for the introduction of such technological solutions. To do this the way in which the new transport solutions affect the local context is analysed. In particular, this work aim to identify the most relevant attributes which influence the decision processes on the adoption of such technological solutions, with reference to their impact on the territory and on the economic activities. To do this, the analysis focuses on the effects involved by the use of wireless technologies and radio frequency identification into seaport infrastructures. Such technologies enable an easier identification of goods in transport terminals; this implies advantages in the organization of the terminal activities, allowing lower time and costs for handling, and at the same time it ensures a greater compliance to security requirements, thus upgrading the level of the performances in these transport systems. On the other hand, the effects of the improvements in transport systems affect the economic context in which transport infrastructures are set. Thus, the adoption of such a technological innovation can represent the chance for local development of the region, due to the better performances of the transport system and to the consequent increased territorial accessibility.

Evolving to Digital and Programmable Value Based Economy: General Prospect and Specific Applications over Sustainability

[eng] In the fields of economics, business and management, how could Digital Transformation (DT) advance value creation and reliably encourage value capture, exchange and distribution? This thesis aim to fill that gap with a novel framework to support policy-makers, countries, cities and businesses address the potential value that can be generated and captured by digitalization combining DT and Internet of Value theoretical perspectives and practical applications of them over concrete issues such as sustainability in cities, as an example. For this, it is proposed to make new contributions related to DT and Internet of Value in two main aspects: to explore DT countries’ mindsets when it relates to their value progress through Digital Ecosystems and to advance with the potential digital value applications through Programmable Economy advantages when it focus on concrete aspect such as sustainability in cities. Both perspectives, although it will be applied on different dimensions and on different purposes, have in common that they are focus on digital and programable value based economy and management and want to explore the best way to maximize and capture the DT potential in terms of value for organizations and society. Thus, first, it will be analysed the importance of knowing clearly the digital ecosystem in which the agents are operating in order to reinforce the value creation by promoting the inclusivity and connectivity of the endpoints involved in it. Secondly, it will be analysed how the digital value can be captured, exchanged and redistributed in a complex issues such as sustainability by deploying concrete digital applications that include human reinforcement aspects to, finally, closing the circle combining both perspectives in a single framework. To achieve these objectives in this thesis, own models are proposed, inspired by other theoretical models already contrasted, and some proven methodologies are used related to Conditional Probability, Forgotten Effects and Fuzzy Sets. As a main conclusion, Digital Transformation has the potential to generate immense value for economy and society. Although currently the capture of the vast majority of it is not guaranteed and its distribution between agents is no clear, new formulas are being explored supported by the Internet of Value. This thesis defends that if agents want to advance value creation and encourage value capture, they should consider to make their own Digital and Programmable Value Based Economy and Management framework through: - Allowing all functional agents work in a Digital Ecosystem embracing new relationships and ways of collaborating pursuing the same purpose. - Deploying Programmable Economy applications advantages, mixing digital's and analogue's world that can be interlinked and programmed by the blockchain allowing monetization and exploring new human and machine alliances. - Adopting strong and inclusive agents’ commitment in order to exploit the advantages that this smart economy system has from a human centric vision, discovering new forms of value, considering that, although tech can be everywhere, value not

Decision making on adoption of cloud computing in e-commerce using fuzzy TOPSIS

© 2017 IEEE. Cloud computing promises enhanced scalability, flexibility, and cost-efficiency. In practice, however, there are many uncertainties about the usage of cloud computing resources in the e-commerce context. As e-commerce is dependent on a reliable and secure online store, it is important for decision makers to adopt an optimal cloud computing mode (Such as SaaS, PaaS and IaaS). This study assesses the factors associated with cloud-based e-commerce based on TOE (technological, organizational, and environmental) framework using multi-criteria decision-making technique (Fuzzy TOPSIS). The results show that Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach proposes software-as-a-service (SaaS) as the best choice for e-commerce business

Artificial intelligence applied to potential assessment and talent identification in an organisational context

França, T. J. F., São Mamede, J. H. P., Barroso, J. M. P., & Santos, V. M. P. D. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organisational context. Heliyon, 9(4), 1-25. [e14694]. https://doi.org/10.1016/j.heliyon.2023.e14694Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive synthesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increasingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and recommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.publishersversionpublishe

Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh

© 2018 Elsevier Ltd Recently, big data (BD) has attracted researchers and practitioners due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular among manufacturing companies as it helps gain insights and make decisions based on BD. However, there many barriers to the adoption of BDA in manufacturing supply chains. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Previous studies have mostly built conceptual frameworks for BDA in a given situation and have ignored examining the nature of the barriers to BDA. Due to the significance of both BD and BDA, this research aims to identify and examine the critical barriers to the adoption of BDA in manufacturing supply chains in the context of Bangladesh. This research explores the existing body of knowledge by examining these barriers using a Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies. The findings of this research are as follows: (i) data-related barriers are most important, (ii) technology-related barriers are second, and (iii) the five most important components of these barriers are (a) lack of infrastructure, (b) complexity of data integration, (c) data privacy, (d) lack of availability of BDA tools and (e) high cost of investment. The findings can assist industrial managers to understand the actual nature of the barriers and potential benefits of using BDA and to make policy regarding BDA adoption in manufacturing supply chains. A sensitivity analysis was carried out to justify the robustness of the barrier rankings

Improved resource efficiency and cascading utilisation of renewable materials

In light of various environmental problems and challenges concerning resource allocation, the utilisation of renewable resources is increasingly important for the efficient use of raw materials. Therefore, cascading utilisation (i.e., the multiple material utilisations of renewable resources prior to their conversion into energy) and approaches that aim to further increase resource efficiency (e.g., the utilisation of by-products) can be considered guiding principles. This paper therefore introduces the Special Volume “Improved Resource Efficiency and Cascading Utilisation of Renewable Materials”. Because both research aspects, resource efficiency and cascading utilisation, belong to several disciplines, the Special Volume adopts an interdisciplinary perspective and presents 16 articles, which can be divided into four subjects: Innovative Materials based on Renewable Resources and their Impact on Sustainability and Resource Efficiency, Quantitative Models for the Integrated Optimisation of Production and Distribution in Networks for Renewable Resources, Information Technology-based Collaboration in Value Generating Networks for Renewable Resources, and Consumer Behaviour towards Eco-friendly Products. The interdisciplinary perspective allows a comprehensive overview of current research on resource efficiency, which is supplemented with 15 book reviews showing the extent to which textbooks of selected disciplines already refer to resource efficiency. This introductory article highlights the relevance of the four subjects, presents summaries of all papers, and discusses future research directions. The overall contribution of the Special Volume is that it bridges the resource efficiency research of selected disciplines and that it presents several approaches for more environmentally sound production and consumption

Continuous monitoring of enterprise risks: A delphi feasibility study

A constantly evolving regulatory environment, increasing market pressure to improve operations, and rapidly changing business conditions are creating the need for ongoing assurance that organizational risks are continually and adequately mitigated. Enterprises are perpetually exposed to fraud, poor decision making and/or other inefficiencies that can lead to significant financial loss and/or increased levels of operating risk. Increasingly, Information Systems are being harnessed to reinvent the risk management process. One promising technology is Continuous Auditing, which seeks to transform the audit process from periodic reviews of a few transactions to a continuous review of all transactions. However, the highly integrated, rapidly changing and hypercompetitive business environment of many corporations spawns numerous Enterprise Risks that have been excluded from standard risk management processes. An extension of Continuous Auditing is Continuous Monitoring, which is used by management to continually review business processes for unexpected deviations. Using a Delphi, the feasibility and desirability of applying Continuous Monitoring to different Enterprise Risks is studied. This study uncovers a significant relationship between the perceived business value of Continuous Monitoring and years of experience in Risk Management and Auditing, determines that all key architectural components for a Continuous Monitoring system are known, and indicates that Continuous Monitoring may be better suited for monitoring computer crime than monitoring strategic risks such as the loss of a competitive position

Obtenção de dados meteorológicos para sistemas de alerta fitossanitário: o caso da duração do período de molhamento foliar

Disease-warning systems are decision support tools designed to help growers determine when to apply control measures to suppress crop diseases. Weather data are nearly ubiquitous inputs to warning systems. This contribution reviews ways in which weather data are gathered for use as inputs to disease-warning systems, and the associated logistical challenges. Grower-operated weather monitoring is contrasted with obtaining data from networks of weather stations, and the advantages and disadvantages of measuring vs. estimating weather data are discussed. Special emphasis is given to leaf wetness duration (LWD), not only because LWD data are inputs to many disease-warning systems but also because accurate data are uniquely challenging to obtain. It is concluded that there is no single best method to acquire weather data for use in disease-warning systems; instead, local, regional, and national circumstances are likely to influence which strategy is most successful.Os sistemas de alerta fitossanitário são ferramentas de suporte à decisão desenvolvidos para ajudar os agricultures a determinar o melhor momento da aplicação das medidas de controle para combater as doenças de plantas. As variáveis meteorológicas são dados de entrada quase que obrigatórios desses sistemas. Este trabalho apresenta uma revisão sobre os meios pelos quais as variáveis meteorológicas são coletadas para serem usadas como dados de entrada em sistemas de alerta fitossanitário e sobre os desafios associados à logística de obtenção desses dados. Essa revisão compara o monitoramento meteorológico ao nível do produtor, nas propriedades agrícolas, com aquele feito ao nível de redes de estações meteorológicas, assim como discute as vantagens e desvantagens entre medir e estimar tais variáveis meteorológicas. Especial ênfase é dada à duração do período de molhamento foliar (DPM), não somente pela sua importância como dado de entrada em diversos sistemas de alerta fitossanitário, mas também pelo desafio de se obter dados acurados dessa variável. Pode-se concluir, após ampla discussão do assunto, que não há um método único e melhor para se obter os dados meteorológicos para uso em sistemas de alerta fitossanitário; por outro lado, as circunstâncias a nível local, regional e nacional provavelmente influenciam a estratégia de maior sucesso