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The purpose of the study reported is to explore the approaches that multinational companies take in order to handle international differences in their management of quality. Qualitative methods for data collection have been used. The empirical data consist of in‐depth and/or focus group interviews at five multinational companies. The study shows that, although the magnitude of the problems raised by international differences is limited, some important implications exist. The differences that have most impact are those of culture, and especially the cultural dimension of power distance. The issue of centralisation versus decentralisation is discussed. The importance of the commitment of top management and having a common value base throughout the company is highlighted. A framework for handling international differences in a multinational company is proposed.

Quality management systems are commonly applied to meet the increasingly stringent requirements for product quality in discrete manufacturing industries. However, traditional experience-driven quality management methods are incapable of handling heterogeneous data from multiple sources, leading to information islands. This study aims to present a quality management key performance indicator visualization (QM-KPIVIS) system to enable integrated quality control and ultimately ensure product quality.

Based on multiple heterogeneous data, an integrated approach is proposed to quantify explicitly the relationship between Internet of Things data and product quality. Specifically, this study identifies the tracing path of quality problems based on multiple heterogeneous quality information tree. In addition, a hierarchical analysis approach is adopted to calculate the key performance indicators of quality influencing factors in the quality control process.

Proposed QM-KPIVIS system consists of data visualization, quality problem processing, quality optimization and user rights management modules, which perform in a well-coordinated manner. An empirical study was also conducted to validate the effectiveness of proposed system.

To the best of the authors’ knowledge, this study is the first attempt to use industrial Internet of Things and multisource heterogeneous data for integrated product quality management. Proposed approach is more user-friendly and intuitive compared to traditional empirically driven quality management methods and has been initially applied in the manufacturing industry.

Editorial This special issue of Industrial Management & Data Systems is a huge departure from our usual journal/ monograph style. This is an additional issue to the year's volume — a bonus in fact.

This study aims to identify service quality dimensions for logistics service providers (LSPs) and to examine their relationships with customer satisfaction and customer loyalty.

Service quality dimensions are identified from vast literature review. Customers who take services from LSPs were surveyed to collect data on basis of developed survey instrument. Structural Equation Modelling (SEM) is applied to test the proposed research hypotheses.

The study shows that all the five service quality constructs, i.e. “Operational Quality”, “Resource Quality”, “Information Quality”, “Personnel Contact Quality” and “Customization and Innovation Quality” have direct relationship with customer satisfaction. They also have indirect relationship with customer loyalty, implying the full mediation of customer satisfaction.

The results of the study suggest that the logistics service quality (LSQ) can be measured multi-dimensionally. It provides clear implications to LSPs for improvement of service quality. The present research work is expected to be useful for both, logistics service providers and the customer organizations, which take services from LSPs. LSPs can develop strategies to improve their service quality on basis of findings from this study.

The present research will help in extending the existing literature on service quality in context to LSPs.

A commonly suggested measure to make logistics greener is a shift to intermodal road‐rail transportation. Most research addresses the issue from the carrier's perspective, arguing for ways to improve the service production to better fit the shippers' demand. In this article the issue is addressed from the shipper's perspective. The purpose is to understand what contextual factors and operations changes that are possible and/or necessary for the shipper to make a fit to the current production system.

Six case companies selling non‐bulk, fast moving goods are examined. These firms have gone against the mainstream and shifted modes of transport. They are investigated through a multiple case‐study design.

The findings indicate that contextual factors stressed in the carrier‐focused literature, or rule of thumb decisions made by shipping logistics management, do not always clearly predict the success of a modal shift. However, some common denominators emerge among successful cases: large transport purchasing resources, high general carrier performance, low demand volatility, and centralized system control. The study also poses some propositions regarding the success of a modal shift.

The research is qualitative in nature and thus limited to the companies and their respective logistics systems. However, the models could be further evaluated empirically through quantitative and qualitative methods alike.

The paper poses a number of propositions of what constitutes a successful modal shift from a shipper's perspective, based on the identified factors and operational changes.

Previous research on the shift to intermodal road‐rail solutions are predominantly made from a carrier's perspective. This research addresses the issue from the shipper's perspective.

The purpose of this article is to examine some of the considerations in the preparation of professionals for service quality and organizational excellence.

Effectiveness, rather than academic principles, is the basis of methodologies for quality excellence.

The conventional approach to quality is rooted in manufacturing which has served its purpose, but is not necessarily effective in modern days.

The work is not based on empirical data, but on the rationale behind quality management.

Reasoning of this paper would have important implications on the way education and training of future professionals are trained, especially those for quality of service.

The concept of “customer satisfaction” must be viewed in a different context vis-à-vis the new realities of globalization and importance of service industries.

This is a subject that has not gained sufficient attention before, though the implications of education and training are far reaching.

The purpose of this paper is to report on the findings of a study designed to understand how management accounting changes when an organization evolves in a more network‐oriented direction, informed by an experiential or integrated approach to learning.

This paper draws on a longitudinal case study initiated by the findings from a previous action research study at a manufacturing plant, the Volvo Floby factory.

By introducing and elaborating on the concept of local management accounting system (LMAS), this paper contributes to the debate about how management accounting reacts to new organizational forms and technologies. Two types of management accounting information are identified: one corresponds to the accountability aspect of accounting – Type 1 information, while Type 2 information refers to problem solving and control. A LMAS is supportive of both local accountability, which draws on valuing skills, and of local problem solving, which relies on decision skills.

One of the research implications of this study is that a LMAS may function as an important mechanism for innovation by explicitly integrating the holistic and the analytic dimensions of experiential learning.

The reported case illustrates how management accounting may facilitate process innovation in a practical setting.

By adopting an experiential learning methodology to develop new knowledge, the production plant studied has succeeded in creating an environment characterized by continuous innovation.

The purpose of the paper is to develop a framework for low‐cost sourcing assessment and to explore the consequences of low‐cost sourcing in China for a European manufacturer.

The low‐cost sourcing framework generated from literature and the consequence analysis is based on a case study of a European company that has outsourced part of its casting processes to Chinese suppliers.

Characteristics of low‐cost sourcing are based on a literature review divided into three categories: country characteristics, supply network structure, and supply network relationships and the case study shows that these three categories of characteristics jointly create negative effects. A two‐directional cause and effect relationship is proposed between the characteristics and the operational supply chain performances. The presented low‐cost sourcing assessment framework should be a good starting point for low‐cost sourcing assessment, including mapping a firm's total characteristics, and for analysing their performance impact.

The conducted single case study is not enough for identifying, formulating, and validating all existing relationships between the low‐cost sourcing characteristics. The present study has identified the existence of the relationships but has not evaluated their levels of impact.

Managers should be aware of how suppliers in low‐cost countries may affect the structures, relations, and operational supply chain performances of the supply network. This paper presents a sourcing assessment framework enabling describing what dimensions of the sourcing characteristics would be affected by sourcing to a specific area of the world and what consequences and performance effects this would have.

Few prior studies have focused on companies with already established relationships with low‐cost‐country suppliers and how these companies should make the best out of these supply chains. This study takes a holistic perspective on low‐cost sourcing and identifies several streams for further research.

While few would disagree that high data quality is a precondition for the efficiency of a company, this remains an area to which many companies do not give adequate attention. Thus, this paper aims to identify which are the most important barriers preventing companies from achieving high data quality. By improving awareness of barriers on which to concentrate, companies are put in a better position to achieve high quality data.

First, a literature review of data quality and data quality barriers is carried out. Based on this literature review, the paper identifies a set of overall barriers to ensuring high data quality. The significance of these barriers is investigated by a questionnaire study, which includes responses from 90 Danish companies. Because of the fundamental difference between master data and transaction data, the questionnaire is limited to focusing only on master data.

The results of the survey indicate that a lack of delegation of responsibilities for maintaining master data is the single aspect which has the largest impact on master data quality. Also, the survey shows that the vast majority of the companies believe that poor master data quality does have significant negative effects.

The contributions of this paper represent a step towards an improved understanding of how to increase the level of master data quality in companies. This knowledge may have a positive impact on the data quality in companies. However, since the study presented in this paper appears to be the first of its kind, the conclusions drawn need further investigation by other research studies in the future.

This paper identifies the main barriers for ensuring high master data quality and investigates which of these factors are the most important. By focusing on these barriers, companies will have better chances of increasing their data quality.

The study presented in this paper appears to be the first of its kind, and it represents an important step towards understanding better why companies find it difficult to achieve satisfactory data quality levels.