Search results

The purpose of this paper is to describe the control and regulation of input DC voltages of nine‐level neutral point clamping (NPC) voltage source inverter (VSI).

The analysis and simulation of a cascade made up of three‐phase five‐level PWM rectifier‐nine levels NPC VSI are treated. This cascade is used to feed a permanent magnet synchronous machine (PMSM) drive. First, the five‐level PWM rectifier is presented. Then a topology of nine‐level NPC VSI and the associated PWM control strategy are described. In order to discard the problem of DC link voltage fluctuations, a clamping bridge with a PI regulation has been added to the cascade. Then a field‐oriented control strategy has been implemented in the PMSM.

The obtained results are full of promise to use the inverter in high voltage and great power applications such as electric naval propulsion systems.

The application of the proposed feedback control algorithm to the studied cascade offers the possibility of stabilizing the DC voltages. The studied cascade absorbs network currents with low‐harmonic content and unity power factor. In all, the instability problems associated with use of multilevel inverters are solved.

To be more effective, artificial intelligence (AI) requires a broad overall view of the design and transformation of enterprise architecture and capabilities. Maturity models (MMs) are the recognized tools to identify strengths and weaknesses of certain domains of an organization. They consist of multiple, archetypal levels of maturity of a certain domain and can be used for organizational assessment and development. In the case of AI, quite a few numbers of MMs have been proposed. Generally, the links between AI technology, AI usage and organizational performance stay unclear. To address these gaps, this paper aims to introduce the complete details of the AI maturity model (AIMM) for AI-driven platform companies. The associated AI-Driven Platform Enterprise Maturity framework proposed here can help to achieve most of the AI-driven platform companies' objectives.

Qualitative research is performed in two stages. In the first stage, a review of the existing literature is performed to identify the types, barriers, drivers, challenges and opportunities of MMs in AI, Advanced Analytics and Big Data domains. In the second stage, a research framework is proposed to align company value chain with AI technologies and levels of the platform enterprise maturity.

The paper proposes a new five level AI-Driven Platform Enterprise Maturity framework by constructing a formal organizational value chain taxonomy model that explains a vast group of MM phenomena related with the AI-Driven Platform Enterprises. In addition, this study proposes a clear and precise description and structuring of the information in the multidimensional Platform, AI, Advanced Analytics and Big Data domains. The AI-Driven Platform Enterprise Maturity framework assists in identification, creation, assessment and disclosure research of AI-driven platform business organizations.

This research is focused on the basic dimensions of AI value chain. The full reference model of AI consists of much more concepts. In the last few years, AI has achieved a notable drive that, if connected appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in machine learning, especially in deep neural networks, the entire community stands in front of the barrier of explainability. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models in industry. Our prospects lead toward the concept of a methodology for the large-scale implementation of AI methods in platform organizations with fairness, model explainability and accountability at its core.

AI-driven platform enterprise maturity framework can be used for better communicate to clients the value of AI capabilities through the lens of changing human-machine interactions and in the context of legal, ethical and societal norms.

The authors discuss AI in the enterprise platform stack including talent platform, human capital management and recruiting.

The AI value chain and AI-Driven Platform Enterprise Maturity framework are original and represent an effective tools for assessing AI-driven platform enterprises.

This paper presents a method for adapting an Information Security Focus Area Maturity (ISFAM) model to the organizational characteristics (OCs) of a small- and medium-sized enterprise (SME) cluster. The purpose of this paper is to provide SMEs with a tailored maturity model enabling them to capture and improve their information security capabilities.

Design Science Research was followed to design and evaluate the method as a design artifact.

The method has successfully been used to adapt the ISFAM model to a group of SMEs within a regional cluster resulting in a model that is aligned with the OCs of the cluster. Areas for further investigation and improvements were identified.

The study is based on applying the proposed method for the SMEs active in the transport, logistics and packaging sector in the Port of Rotterdam. Future research can focus on different sectors and regions. The method can be used for adapting other focus area maturity models.

The resulting adapted maturity model can facilitate the creation and further development of a base of common or shared knowledge in the cluster. The adapted maturity model can cut the cost of over implementation of information security capabilities for the SMEs with scarce resources.

The resulting adapted maturity model can facilitate the creation and further development of a base of common or shared knowledge in the cluster. The adapted maturity model can cut the cost of over implementation of information security capabilities for the SMEs with scarce resources.

Ongoing research and development into the implementation of concurrent engineering(CE) within the construction industry have made researchers think how to make CE implementation better, more effective, and more efficient. This has led researchers to investigate CE implementation efforts within other industry sectors, which suggest to carryout a CE readiness assessment of a construction organisation before the adoption of some CE aspects within the industry that has facilitated the CE adoption in other industries. Now the problem is that there is no model or tool available, which will help in assessing the readiness of the organisation. Therefore, this paper discusses the development of a CE readiness assessment model for the construction industry. It also includes a comparative review of existing readiness assessment tools and models that have been specifically developed and successfully used in the manufacturing andIT sectors. It argues that readiness assessment of a construction organisation is a necessity for the implementation of CE in construction and assesses the applicability of existing tools and models to the construction industry. And finally the development of a new readiness assessment model for the construction industry called “BEACON” is presented.

The purpose of this paper is to propose an approach for managing relevant factors and activities for implementing data governance in an organization. The process of assessing the establishment of data governance in an organization is intrinsically imprecise, due to the characteristics of new problem settings, particularly in relation to newly generated alternatives or vaguely defined qualitative assessment criteria.

To reject the inherent subjectiveness and imprecision involved in the evaluation process, the authors use the concept of fuzzy logic in this approach for developing the assessment model and analyzing the model for allocating the management efforts in the most efficient way to improve the data governance deployment level.

This paper identifies relevant factors and activities for implementing data governance in an organization and evaluates the state of data governance based on causal relationships between influential factors. In this study, factors are prioritized for effective allocation of limited management efforts in any improvement plan.

The interrelationships among factors are contextual and based on the perceptions of experts who may be biased as per their background and area of expertise. Meanwhile, lack of a data governance plan may cause failure during its implementation in an organization, as the worth of an organization's data will not be determined precisely. The paper has tremendous practical implications for organizations that intend to implement the data governance program and evaluate its state to design an improvement plan.

The paper proposes an approach for implementing data governance in an organization faced with limited resources for improvement.

The purpose of this study is to discover valuable customers for enterprises. The international market of Taiwan airlines can be enhanced; thus, this study aims at Taiwan’s airline market as a research area.

This research uses data mining technologies with a proposed model to analyse airline customer values for big data online marketing systems, such as customer relationship management (CRM) system. The research applied supervised apriori algorithm, socio-economic variables and a proposed model to discover the rules/markets.

The results show that eight markets were discovered and three association rules were established for business systems of airlines.

The valuable travellers/markets can be discovered by this research. By collecting shoppers’ transactional data, global online CRM and point of sales (POS) systems can be big data marketing systems. The research framework can be easily applied in online CRM/POS or big data marketing systems for international airlines; however, it is for other global businesses as well.

Due to technology advancement or transparency in system, there is a constant inflow and outflow of technology in the business for transparency and efficiency. To seize a competitive advantage, companies have emerged new technological solutions to respond to the change in the organization environment. There is a surge in the requirement of learning opportunities and effective training programs in the organization. The current study has been an effort to understand the potential of blockchain technology that can create better training evaluation.

The electronic-Delphi (e-Delphi) method has been conducted by recording the final consensus and to find a balance for implementation of blockchain technology and measuring training effectiveness. The current research is one of its new types where blockchain-enabled training effectiveness measurement (BETEM) model has been formulated using a qualitative approach.

The study has considered human resource (HR) professionals as the experts and based on their responses, the formulation of theoretical network model has been structured using e-Delphi–BETEM (e-DLH–BETEM) approach. By critically examining the experts’ responses and comments, the study formulated the four major themes and 11 subthemes for the smooth functioning of the BETEM for an organization.

The research aims to aid innovations in BETEMs model for training evaluation. The model will contribute incrementally toward the complete transformation of the training development programs of employees. The goal of BETEMs is to ensure that organizations, specifically HR personals can prepare themselves to have competitive advantage by using blockchain technology.

The application of blockchain technology in measuring the training effectiveness is an addition to existing literature as majority of existing studies have studied the use of technology for measuring training effectiveness.

This paper seeks to ascertain the effectiveness of a two-week-long faculty development programme (FDP), organized by Indira Gandhi National Open University (IGNOU), for teachers of the open and distance learning (ODL) system, with the aim of upgrading their knowledge and skills for developing print self-learning materials (SLMs). The specific aim of this study is to ascertain whether the objectives of the FDP have been achieved, to examine the effectiveness of the training programme and to suggest measures for improvement in future FDPs regarding the design and development of print SLMs.

The study was conducted using a descriptive survey research method, through semi-structured questionnaires and adopted purposive sampling. The first survey was conducted immediately after the completion of the programme, in order to collect feedback from the trainees, while the second survey was conducted after six months of the FDP, using the follow-up approach, so that more reliable and authentic results could be obtained.

The outcomes of the study revealed that the training had been effective in imparting appropriate knowledge and skills to the trainees, with respect to designing print SLMs. However, certain gaps were also identified and have been reported in this paper. Key suggestions have been made to address the shortcomings and improve forthcoming FDPs.

The present research focused on a specific training programme regarding the design and development of SLMs. Therefore, only the teachers and academics who participated in this specialized training activity were considered for the collection of feedback.

The recommendations of this work may be useful for the trainers, FDP coordinators, training institutions and ODL policymakers for planning and designing effective staff development activities for teachers and academics working in ODL institutions. These would be especially useful in the design of FDPs, aimed at orienting teachers who are involved in curriculum planning, design and development of learning resources.

This study is an original research based on the empirical primary data obtained by the researchers. As the largest open university in the world, IGNOU has been playing a key role in staff development for and in ODL in developing countries. This evaluative study of a specialized FDP in the area of SLM design and development is a significant work that may be valuable for planning the staff development strategies and building a training evaluation mechanism.

The purpose of this research is to expand cross‐cultural research and validate the Learning Transfer System Inventory in Ukraine. The researchers seek to translate the LTSI into Ukrainian and investigate the internal structure of this translated version of the questionnaire.

The LTSI is translated into Ukrainian using a rigorous translation methodology. The instrument was distributed to 511 (430 – usable data) individuals from various organizations throughout Ukraine. The exploratory factor analysis (common factor analysis with oblique rotation) was used to analyze the survey data.

The factor structure of the Ukrainian version of the LTSI (ULTSI) paralleled that of the original instrument. Most items loaded on appropriate factors with loadings over 0.4. Two factors (Opportunity to Use Learning and Performance‐Outcomes Expectations) did not emerge as clearly as in the original instrument and require further research.

The instrument was distributed to a convenience sample which limits the external validity of the study. Some translation issues may have possibly influenced low loadings for few items. The questionnaires distributed in the Eastern part of Ukraine were problematic because the population there is mostly Russian‐speaking while the instruments were in Ukrainian. The study provides evidence of construct validity of the LTSI in the Ukrainian business environment. With the limitations outlined above further research can address translation issues and language barrier problems. Some items may be included that will suit the Ukrainian environment better, which may resolve problems in two factors mentioned above. New methods of instrument translation could be utilized in future as well as alternative statistical analyses (Confirmatory Factor Analysis).

The ULTSI is now one of the first and few HRD tools in Ukraine. It can be used to assess the transfer climate in Ukrainian organizations in order to maximize the positive outcomes of the investment in training. As a diagnostic tool the ULTSI can provide necessary information about the environmental and organizational forces that are at play in a given organization, and it can help improve the results of training interventions.

This cross‐cultural study is one of the first efforts in existence to bridge the Ukrainian and Western cultures in terms of HRD. It provides a possibility for HRD methodology and theory to be introduced in Ukrainian businesses. At the same time, the study provides evidence of the construct validity and sound structure of the LTSI.

This paper questions the validity of traditional scientific method for the study of human learning and proposes five postulates for the advancement of a conversational science. It considers how an evolving capacity for lifelong learning has been constrained by inappropriate research methods and educational practice leading to a learning deficit in the population. Over 25 years of action research offers solid evidence for the humanisation of science as a conversational research process which respects the individual as a unique meaning constructing, self‐organising learning (SOL) entity. A learning conversation pedagogy which enables learners to act as personal scientists and action researchers and a SOL Systems Seven for a community of action researchers is outlined. Finally, the paper considers how SOL entities can function as catalysts for new forms of ORDER with a potential for the emergence of a new species of learning and of being human.