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Many manufacturers are transforming into manufacturing service industry to enhance their value creation. Adopting the value‐added chain model, this study aimed to conduct four case studies, including Acer, Giant, TSMC and Eternal, to verify the high‐valued strategies and the common characteristics of service provisions.

Four case studies, including Acer, Giant, TSMC and Eternal, were conducted to verify the high‐valued strategies and the common characteristics of service provisions. Specifically, these companies are selected from different industry and value chain position to enhance the robustness of the research findings.

This study concluded that the manufacturing firms strengthen their position as system integrator. The provision of high‐valued services is orientated toward the integration of the value chain stages according to the industry and business model. The companies are going to upstream or downstream, outsource non‐core manufacturing activities, and sell some manufacturing assets. The high‐valued service strategies provided the manufacturing firms with new approaches to compete in a rapidly changing economy. The findings also provided the direction for the emerging economies in confronting with industrial structure transformation.

This study focuses on the transformation of four manufacturing firms toward providing high value‐added services. The results conclude that manufacturing firms can integrate forward and backward stages in the value‐added chain, and provide the knowledge‐based services including R&D, marketing, information system, branding, financial and after‐sale services to enhance the market value of their products. This study argues that the high value‐added service strategies can be a great opportunity for the manufacturers.

The purpose of this paper is to provide a more robust understanding of the development of dynamic capabilities (DCs) in service multi-units with different cultural distances (CD) (high (HCD) and low (LCD)) through the routines of embedded social capital (structural and relational) and knowledge archetype (exploitative and exploratory) learning.

The authors used survey questionnaires and structural equation modeling to discriminate the relationships among variables.

The authors found that structurally embedded social capital has a positive influence on exploratory knowledge learning in HCD service multi-units; relationally embedded social capital has a positive influence on knowledge archetype (exploitative vs exploratory) learning in both HCD and LCD service multi-units; and knowledge archetype learning has a positive influence on the development of DCs in both HCD and LCD service multi-units.

The results identify the central role of social capital (structurally and relationally embedded) in enabling knowledge archetype learning and the development of DCs in service multi-units. In addition, this study provides a description and comparison of how structurally and relationally embedded social capital are key antecedents in knowledge archetype learning and the development of DCs in the context of service multi-units with different HCD and LCD.

The results provide a practical trajectory for the development of DCs in multi-units of multinational corporations in the service industry with different HCD and LCD.

The purpose of this paper is to provide a more robust understanding of how to deploy multiunit organizations’ dynamic capabilities (DCs) by examining the roles of embedded social (structural and relational) capital and knowledge archetype (exploitative and exploratory) learning.

This study uses 315 multiunit samples and structural equation modeling to determine the relationships among the variables.

The analysis reveals that, while embedded structural social capital exerts a positive influence on exploratory knowledge learning in multiunit organizations, embedded relational social capital exerts a positive influence on knowledge archetype (exploitative and exploratory) learning. Knowledge archetype (exploitative and exploratory) learning also positively influences DC deployment in multiunit organizations.

Few DCs studies have empirically examined the roles of embedded social (structural and relational) capital and knowledge archetype (exploitative and exploratory) learning in multiunit organizations. The results of this study address the failure of past theoretical perspectives on DCs to fully specify and verify the links between the roles of embedded social (structural and relational) capital and knowledge archetype (exploitative and exploratory) learning.

This paper offers one practical trajectory for DC deployment in modern multiunit organizations and offers two contributions to the theoretical perspectives on DCs. First, it identifies the critical role of embedded social capital in enabling knowledge archetype learning and DC deployment, which had never been fully specified or verified in the DCs literature. Second, it identifies the importance of DCs’ deployment trajectory in multiunit organizations’ routine processes.

This paper (Part 2 of 2) aims to shortlist performance indicators which are used in evaluating facilities operation and maintenance (O&M) in commercial buildings.

A three-session focus group meeting, with the use of a customized questionnaire and audio recording, was convened to solicit opinions from O&M experts. Their quantitative responses (ratings on the importance of the indicators) and the qualitative ones (reasons for having the indicators selected or excluded) were taken for analysis, followed by mapping the shortlisted indicators based on the phase-hierarchy (P-H) model for facilities management (FM).

From a total of 74 performance indicators (71 identified from the literature and three added by the focus group), 17 indicators were shortlisted, and time constraint was the most common reason for having those indicators excluded from the shortlist. Mapping the shortlisted indicators with the P-H model revealed that the performance evaluation focus of the experts was at the tactical level, on the output phase of facilities services delivery.

The shortlisted indicators serve as a keystone for establishing a performance evaluation scheme for engineering facilities in commercial buildings. Research on other areas may follow the approach of this study to shortlist key performance indicators (KPIs).

Professionals of the other building types (e.g. residential, industrial and healthcare) or sectors with diverse FM organizations may conduct a similar study to identify indicators for performance evaluation purposes. In particular, the process of shortlisting the O&M KPIs may be used to shortlist KPIs for the other FM services.

The focus group study demonstrates how to rigorously select KPIs for use in managing facilities.

The purpose of this paper (Part 1 of 2) is to classify and map, in a systematic manner and from a facilities management (FM) perspective, the performance indicators that are applicable to evaluating facilities operation and maintenance (O&M) in commercial buildings.

Forming part of a multi-stage research project, the applicable performance indicators that had been identified from an extensive literature review were consolidated and defined. Based on a phase-hierarchy (P-H) model – a fundamental classification framework comprising three phases of facilities services delivery and three hierarchical FM levels – the indicators were systematically classified, and a map showing their distribution along the phase and hierarchy dimensions was obtained.

The P-H model enabled systematic classification of the 71 applicable indicators. Mapping the indicators with the model showed that more indicators concern the input or output phase of facilities services delivery. Indicators at the strategic level, which have a wide span of control, are small in quantity, compared to the large number of indicators at the operational level.

The P-H model, which proves useful for classifying performance indicators for facilities in commercial buildings, may be applied to similar research on other types of buildings or infrastructures.

The method of classifying the performance indicators and the mapping result of the indicators are useful reference for different levels of FM practitioners.

This paper illustrates a novel attempt that made use of the P-H model to classify O&M performance indicators.

The existence of weak‐form efficiency in the equity markets of the three main Central European transition economies (the Czech Republic, Hungary, and Poland) is examined for the period July 1995 through September 2000, using weekly Investable and Comprehensive indexes developed by the International Finance Corporation. Several different approaches are used. Univariate and multivariate tests provide some evidence that stock prices in these exchanges exhibit a random walk, which constitutes evidence for weakform efficiency. This differs in some cases from studies using data for the initial years of these markets. The variance ratio test (VR) of Lo and MacKinlay (1988) yields somewhat mixed results concerning the random‐walk properties of the indexes. A modelcomparison test compares forecasts from a NAÏVE model with ARIMA and GARCH alternatives. Results from the model‐comparison approach are consistent in rejecting the random‐walk hypothesis for the three Central European equity markets.

The purpose of this paper is to propose an approach to achieve better accuracy in technology forecasting (TF) by providing the concepts of the service components and service composition based on the theory of the combining forecasts. Next, it adopts three quantitative analyses as service components to form service composition. This will support the need of more predictable TF, which raises the accuracy of the quantitative analysis and, at the same time, presents the service composition logic in a consistent manner in the form of customized TF.

This paper provides a systematic analysis of the technology forecasts for third‐generation (3G) telecommunication industry. This systematic approach mainly unifies the Bass model, logit model, and least squares analysis forecasting techniques, along with a reasonable assessment of the scope for the normal curve (±1 standard deviation), and attempts to find the maximum possibility frontier of the predictive value.

Through the integration and comparison of these three techniques, not only can the predicted values of the three forecasting methods be determined, but a preferred solution can also be derived through new methods, and in return, to investigate better accuracy and performances. Such an approach can also integrate the advantages of various methods to provide a prediction interval, as well as objective and realistic projections.

This envisaged concept of “service component and service composition” is an integration of backing up in TF instruments in selection and reselection, which in return, provide optimization of service composition and accuracy maximization, as well as better performance prediction. A well‐known limitation of this research is that sudden technology breakthroughs are often unforeseeable in the majority of main‐stream quantitative analyses.

Constructing a new effective approach as results of “service component and service composition” can be compared to the traditional research methods such as Delphi method or other mathematical algorithms. This method generally produces higher quality forecasts than those attained from a single source.

The article intended to excavate the Lean Six Sigma (LSS) deployment challenges, Critical Success Factors (CSF), tools and techniques, and managerial implications in an Indian healthcare setting.

The article illustrates a case study established using Action Research (AR) approach. Further, the case study is based on the Define, Measure, Analyze, Improve, Control (DMAIC) phases of LSS. The performance and service quality of the Endodontics department of a dental college attached to a hospital is enhanced and sustained through the LSS strategy.

The processing time of Root Canal treatment is reduced by determining the root causes for delay and implementing sustainable solutions. The structured deployment of the LSS strategy helped the Endodontics department to reduce the processing time from an average of 116 min–84 min. Thus, the process's sigma level is enhanced from 0.06 to 4.17 and assisted in sustaining the results.

The case study's findings are based on the single AR carried out at an Endodontics department of a dental college hospital based on LSS strategies. Even though this study's results cannot be generalized, the deliverables of the case study can be used to develop the LSS roadmap for the dental colleges to enhance the service quality and safety of the patients.

The article provides step-by-step details for implementing LSS in dental college hospitals with critical analysis based on robust statistical tools and techniques. The case study provides evidence of the adoption of LSS in medical college education and provides the confidence to adopt the same through novice users. The study's findings may persuade the policymakers to add LSS in the medical education curriculum to reinforce safety and reduce errors in the healthcare system.