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The purpose of this paper is to conceptualize data quality (DQ) in the context of business process management and to propose a DQ oriented approach for business process modeling. The approach is based on key concepts and metrics from the data quality management domain and supports decision‐making in process re‐design projects on the basis of process models.

The paper applies a design oriented research approach, in the course of which a modeling method is developed as a design artifact. To do so, method engineering is used as a design technique. The artifact is theoretically founded and incorporates DQ considerations into process re‐design. Furthermore, the paper uses a case study to evaluate the suggested approach.

The paper shows that the DQ oriented process modeling approach facilitates and improves managerial decision‐making in the context of process re‐design. Data quality is considered as a success factor for business processes and is conceptualized using a rule‐based approach.

The paper presents design research and a case study. More research is needed to triangulate the findings and to allow generalizability of the results.

The paper supports decision‐makers in enterprises in taking a DQ perspective in business process re‐design initiatives.

The paper reports on integrating DQ considerations into business process management in general and into process modeling in particular, in order to provide more comprehensive decision‐making support in process re‐design projects. The paper represents one of the first contributions to literature regarding a contemporary phenomenon of high practical and scientific relevance.

The purpose of the paper is to propose a reference model describing a holistic view of the master data lifecycle, including strategic, tactical and operational aspects. The Master Data Lifecycle Management (MDLM) map provides a structured approach to analyze the master data lifecycle.

Embedded in a design oriented research process, the paper applies the Component Business Model (CBM) method and suggests a reference model which identifies the business components required to manage the master data lifecycle. CBM is a patented IBM method to analyze the key components of a business domain. The paper uses a participative case study to evaluate the suggested model.

Based on a participative case study, the paper shows how the reference model makes it possible to analyze the master data lifecycle on a strategic, a tactical and an operational level, and how it helps identify areas of improvement.

The paper presents design work and a participative case study. The reference model is grounded in existing literature and represents a comprehensive framework forming the foundation for future analysis of the master data lifecycle. Furthermore, the model represents an abstraction of an organization's master data lifecycle. Hence, it forms a “theory for designing”. More research is needed in order to more thoroughly evaluate the presented model in a variety of real‐life settings.

The paper shows how the reference model enables practitioners to analyze the master data lifecycle and how it helps identify areas of improvement.

The paper reports on an attempt to establish a holistic view of the master data lifecycle, including strategic, tactical and operational aspects, in order to provide more comprehensive support for its analysis and improvement.

Interest in management development is mushrooming. The number of articles which address different aspects of it are likewise increasing apace. This has heightened the need for a broad‐based review which will pull the material together, give shape to it, evaluate it and draw out its implications. In this, the first of a two‐part article, this task is commenced.

Non-oriented electrical steel presents anisotropic behaviour. Modelling such anisotropic behaviour has become a necessity for accurate design of electrical machines. The main aim of this study is to model the magnetic anisotropy in the non-oriented electrical steel sheet of grade M400-50A using a phenomenological hysteresis model.

The well-known phenomenological vector Jiles–Atherton hysteresis model is modified to correctly model the typical anisotropic behaviour of the non-oriented electrical steel sheet, which is not described correctly by the original vector Jiles–Atherton model. The modification to the vector model is implemented through the anhysteretic magnetization. Instead of the commonly used classical Langevin function, the authors introduced 2D bi-cubic spline to represent the anhysteretic magnetization for modelling the magnetic anisotropy.

The proposed model is found to yield good agreement with the measurement data. Comparisons are done between the original vector model and the proposed model. Another comparison is also made between the results obtained considering two different modifications to the anhysteretic magnetization.

The paper presents an original method to model the anhysteretic magnetization based on projections of the anhysteretic magnetization in the principal axis, and apply such modification to the vector Jiles–Atherton model to account for the magnetic anisotropy. The replacement of the classical Langevin function with the spline resulted in better fitting. The proposed model could be used in the numerical analysis of magnetic field in an electrical application.

Recent investigations on magnetic properties of non-oriented (NO) steel sheets enhance the comprehension of the magnetic anisotropy behaviour of widely employed electrical sheets. The concept of energy/coenergy density can be employed to model these magnetic properties. However, it usually presents an implicit form which requires an iterative process. The purpose of this paper is to develop an analytical model to consider these magnetic properties with an explicit formulation in order to ease the computations.

From rotational measurements, the anhysteretic curves are interpolated in order to extract the magnetic energy density for different directions and amplitudes of the magnetic flux density. Furthermore, the analytical representation of this energy is suggested based on statistical distribution which aims to minimize the intrinsic energy of the material. The model is finally validated by comparing measured and computed values of the magnetic field strength.

The proposed model is based on an analytical formulation of the energy depending on the components of the magnetic flux density. This formulation is composed of three Gumbel distributions. Every functional parameters of energy density is formulated with only four parameters which are calculated by fitting the energy extracted from measurements. Finally, the proposed model is validated by comparing the computation and the measurements of 9

loci for NO steel sheets at 10 Hz. The proposed analytical model shows good agreements with an average relative error of 27 per cent.

The paper presents an original analytical method to model magnetic anisotropy for NO electrical sheets. With this analytical formulation, the determination of H does not require any iterative process as it is usually the case with this energy method coupled with implicit function. This method can be easily incorporated in finite element method since it does not require any extra iterative process.

While big data creates business value, knowledge on how value is created remains limited and research is needed to discover big data’s value mechanism. The purpose of this paper is to explore value creation capabilities of big data through an alignment perspective.

The paper is based on a single case study of a service division of a large Danish wind turbine generator manufacturer based on 18 semi-structured interviews.

A strategic alignment framework comprising human, information technology, organization, performance, process and strategic practices are used as a basis to identify 15 types of alignment capabilities and their inter-dependent variables fostering the value creation of big data. The alignment framework is accompanied by seven propositions to obtain alignment of big data in service processes.

The study demonstrates empirical anchoring of how alignment capabilities affect a company’s ability to create value from big data as identified in a service supply chain.

Service supply chains and big data are complex matters. Therefore, understanding how alignment affects a company’s ability to create value of big data may help the company to overcome challenges of big data.

The study demonstrates how value from big data can be created following an alignment logic. By this, both critical and complementary alignment capabilities have been identified.

The information needs of the users of literature database systems often come from the task at hand, which is short term and can be represented as a small number of articles. Previous works on recommending articles to satisfy users’ short-term interests have utilized article content, usage logs, and more recently, coauthorship networks. The usefulness of coauthorship has been demonstrated by some research works, which, however, tend to adopt a simple coauthorship network that records only the strength of coauthorships. The purpose of this paper is to enhance the effectiveness of coauthorship-based recommendation by incorporating scholars’ collaboration topics into the coauthorship network.

The authors propose a latent Dirichlet allocation (LDA)-coauthorship-network-based method that integrates topic information into the links of the coauthorship networks using LDA, and a task-focused technique is developed for recommending literature articles.

The experimental results using information systems journal articles show that the proposed method is more effective than the previous coauthorship network-based method over all scenarios examined. The authors further develop a hybrid method that combines the results of content-based and LDA-coauthorship-network-based recommendations. The resulting hybrid method achieves greater or comparable recommendation effectiveness under all scenarios when compared to the content-based method.

This paper makes two contributions. The authors first show that topic model is indeed useful and can be incorporated into the construction of coaurthoship-network to improve literature recommendation. The authors subsequently demonstrate that coauthorship-network-based and content-based recommendations are complementary in their hit article rank distributions, and then devise a hybrid recommendation method to further improve the effectiveness of literature recommendation.