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This paper aims to report findings of up-to-date insights to fill the knowledge gap of lack of theoretical and practical understandings of how knowledge is used in medium-sized enterprises (MEs) for ensuring their performance excellence, healthy survival and growth, particularly using the contextual background of quality improvement as the standing point to concretise the research content and research participants’ mind-set for data collection.

The empirical data were attained by conducting first a multiple-case study and thereafter a structured interview. Insights were obtained through analysing the collected data and triangulating the findings with the contention from the extant literature where available.

A set of approaches for effective quality improvement knowledge (QIK) utilisation in MEs have been identified and attested, as well as prioritised for a clear guidance on their application by practical businesses.

As a pioneering study on the particularly focussed issue, namely, a current knowledge gap – QIK utilisation in MEs, theoretically the research contributes to the enrichment of the current KM and QI literature with a primary focus on knowledge utilisation in MEs. Practically its findings provide insightful guidance to practice on the approaches of QIK utilisation.

A successful supply chain should ensure that all participating members benefit from the marketplace. To achieve this goal, the supply chain members need to improve their competences all the time, which requires a continuous learning process. Thus, mutual learning, through knowledge sharing between the different members, is a necessary approach to increase the competence of supply chain partners. To realise efficient and effective knowledge sharing in a supply chain, this paper aims to explore and formulate a model that supports an enterprise with its management of the supply chain members' knowledge resource sharing (herein referred to as “advanced practice” and includes two levels of knowledge – strategic and operational). The model is based on the theories of supply chain management (SCM) and case‐based reasoning (CBR).

This research follows a conductive and inductive cycle. Firstly, based on the learning expounded through an extensive literature survey regarding SCM and CBR, as well as available empirical applications, the conceptual model is designed. Then the primary stage evaluation will be discussed regarding the feasibility and refinement of the model towards its maturity.

To share knowledge along the supply chain is theoretically sound, but a difficult task to realise in practice, due to the complexity of knowledge sharing between the different organizations.

This research explores one of the important topics in SCM – knowledge sharing within a supply chain, and the model also extends and explores a new tool for this knowledge‐sharing process by applying CBR methodology.

The designed model in this research will provide a practice‐oriented vehicle allowing the supply chain members to share and apply their knowledge.

This research applies CBR in the domain of SCM, it both enriches the available approaches to supply chain performance enhancement and enlarges the application domains of CBR methodology.

The purpose of this paper is to introduce a research exploring the important strategic elements and their prioritisation for e-retailers’ home delivery logistics process efficacy improvement.

The research was completed through focus group, survey and importance-performance analysis.

The research identified, confirmed and prioritised a set of explicitly important strategic elements currently deemed important by e-retailers for ensuring the efficacy of their home delivery logistics process in Chinese marketplace, and also referential to the other similar emerging marketplaces.

The research contributes positively to the enrichment of the theoretical knowledge pool of e-retailers’ logistics performance improvement.

The research findings guide/inform the strategy development and implementation for e-retailers entering and/or operating in Chinese marketplace. And the findings can also be referential to the e-retail strategy development for entering and operating in other emerging markets similar to China’s. This point is particularly meaningful for those e-retailers that want to expand the outreaching and increase the popularity of their businesses in the global marketplace.

Corresponding to the much needed further research on e-retailing home delivery performance improvement, the research provides findings that add substantial new insights into the field, with a particular focus on China, as one of the emerging developing marketplaces.

This paper introduces an empirical application of an experimental model for knowledge management within an organization, namely a case‐based reasoning model for manufacturing vision development (CBRM). The model integrates the development process of manufacturing vision with the methodology of case‐based reasoning. This paper briefly describes the model's theoretical fundamentals and its conceptual structure; conducts a detailed introduction of the critical elements within the model; exhibits a real world application of the model; and summarizes the review of the model through academia and practice. Finds that the CBRM is supportive to the decision‐making process of applying and augmenting organizational knowledge. It provides a new angle to tackle strategic management issues within the manufacturing system of a business operation. Explores a new proposition within strategic manufacturing management by enriching and extending the concept of MV while trying to lead the CBR methodology into a new domain by applying it in strategic management.

This paper aims to illustrate an application of Taguchi method of experimental design (TMED) for the development of a new ignition coil for an automotive vehicle.

The application of TMED for optimisation of manufacturing processes has been widely published in the existing literature. However, the applications of TMED in the design and development of new products are not yet widely reported. This case study presents the results of a designed experiment which utilises a 16‐trial experiment to study 14 design parameters and one interaction. The case study strictly follows a systematic and disciplined methodology outlined in the paper.

The optimal settings of the critical design parameters are determined. The optimal settings have resulted in increased customer satisfaction, improved market share and low defect rate in the hands of customers.

Although the optimal levels are determined from one large experiment, it was unable to determine the true optimal values of each design parameter.

Manufacturers may use TMED to optimise processes (either design or manufacturing) without expensive and time‐consuming experimentation. This case study demonstrates the true power of a well planned and designed experiment over the traditional varying one‐factor‐at‐a‐time approach to experimentation which is rather unreliable, not cost‐effective and may lead to false optimal conditions.

The paper provides an excellent resource for those people who are involved in the design optimisation of a new product.

The purpose of this paper is to contribute to the existing stock return predictability and idiosyncratic risk literature by examining the relationship between stock returns and components derived from the decomposition of stock returns variance at the portfolio and firm levels.

A theoretical model is used to decompose the variance of stock returns into two volatility and two covariance terms by using a conditional Fama-French three-factor model. This study adopts portfolio analysis and Fama-MacBeth cross-sectional regression to examine the relationship between components of idiosyncratic risk and expected stock returns.

The portfolio analysis results show that volatility terms are negatively related to expected stock returns, and alpha risk has the most significant relationship with stock returns. On the contrary, covariance terms have positive relationships with expected stock returns at the portfolio level. Furthermore, the results of the Fama-MacBeth cross-sectional regression show that only alpha risk can explain variations in stock returns at the firm level. Another finding is that when volatility and covariance terms are excluded from idiosyncratic volatility, the relation between idiosyncratic volatility and stock returns becomes weak at the portfolio level and disappears at the firm level.

This is the first study that examines the relations between all the components of idiosyncratic risk and expected stock returns in equal-weighted and value-weighted portfolios. This research also suggests covariance terms of idiosyncratic volatility as new predictors of stock returns at the portfolio level. Moreover, this paper contributes to the idiosyncratic risk literature by examining whether all the four additional components explain all the systematic patterns included in the unconditional idiosyncratic risk.

Decision-making is one of the key technologies for self-driving cars. The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations.

In this research, a probabilistic decision-making method based on the Markov decision process (MDP) is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data. The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states, actions and basic models. Transition and reward models are defined by using a complete prediction model of the surrounding cars. An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.

Results show that, at the given scenario, the self-driving car maintained safety and efficiency with the proposed policy.

This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.

The authors investigate how market quality diverges between foreign firms and domestic firms on the US stock market in response to the Russia–Ukraine conflict.

With an event study approach, the authors compare foreign firms with domestic firms in their market responses over the three-day window around the outbreak of the war. Further, with Difference-in-Difference (DID) analyses, the authors study the change in foreign firms' market quality upon this outbreak in comparison with their domestic counterparts. Finally, the authors compare the foreign firms across firm specific characteristics and home country characteristics.

The authors find that foreign stocks listed in the US experience more severe market quality deterioration compared to the stocks' domestic counterparts. This effect is especially strong for companies from countries considered friendlier towards Russia and companies that are not cross-listed. The authors' findings are consistent with the information asymmetry hypothesis concerning market quality. Moreover, US market investors have more concerns over political risks with non-US-aligned political standings during war times.

The authors' findings are consistent with the information asymmetry hypothesis concerning market quality. Moreover, US market investors have more concerns over political risks over non-US-aligned political standings during war time.

Since both countries in the conflict are in Europe, the US stock market, to a certain degree, becomes a safe haven for capital from Europe and other countries. In the meantime, American Depository Receipts (ADRs) have been important for US investors to create a globally diversified portfolio, and the knowledge regarding ADRs' vulnerability to international geopolitical events is valuable. The author' results are informative for stock market investors to understand the market dynamics for international and domestic companies during this extremely uncertain time.

This is the first study that examines the market quality divergence between foreign firms and domestic firms on the US stock market in response to the Russia–Ukraine conflict. The authors provide novel evidence on the change in ADRs' market quality associated with significant political uncertainty. The authors show that ADRs' market quality is more vulnerable to international geopolitical risks relative to otherwise comparable domestic firms.

This paper aims to explore the developing trend of higher pedagogy, to promote the research of higher pedagogy to be more canonical and scientific and to improve the higher education theory.

The utility research uses a method of quantitative study, namely “content analysis”, to estimate and analyze the higher education theory literature on ten dimensionalities which include theme, author, abstract, keywords, subject, proposing of the issue, definition of key terms, research methods, conclusion, reference and annotations from 1996 to 2006 in China.

The analysis of the selected 250 academic theses and reports in the field of higher education shows that academic research and theoretical standard have been improved, meanwhile academic criterion have been formed by degrees in higher pedagogy within ten years. At the same time, there are still some problems in the papers and reports the authors have selected such as title, key words, proposing of the issue, researching method and conclusion.

This paper sums up the trend of research on higher education in China for the past ten years, and points out the problems in this trend and then proposes ways to improve the research of higher education in China.

The main purpose of this paper is to investigate drivers and barriers of climate change mitigation strategies (CCMS), their linkages and impact in Indian Iron and Steel Industry (IISI) in light of ninth sustainable development goal (building resilient infrastructure, promote sustainable industrialization and foster innovation).

To identify relevant drivers and barriers, a thorough literature review and opinions of industry experts were obtained. Utilizing Total Interpretive Structural Modeling (TISM), the selected drivers and barriers were modeled separately along with Cross Impact Matrix-multiplication Applied to Classification (MICMAC).

Pragmatic and cost-effective technology, less supply chain complexity, robust policy and legal framework were found to have the highest driving power over all the other drivers. Findings suggest political pressure as the most critical barrier in this study. The results from TISM and MICMAC analysis have been used to elucidate a framework for the understanding of policymakers and achieve top management commitment.

This paper will help researchers, academicians, industry analysts and policymakers in developing a systems approach in prioritizing CCMS in energy-intensive (coal dependent) iron and steel plants. The model outcomes of this work will aid operational research to understand the working principles in other industries as well.

To the best of authors' knowledge, there is paucity of reported literature for the drivers and barriers of CCMS in iron and steel industry. This paper can be considered a unique, first attempt to use data from developing nations like India to develop a model and explain relationships of the existing drivers and barriers of CCMS.