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The authors aim to determine the effects of Global Lesson Study (GLS) – a two-year, one-cycle program defined as “international cooperative lesson study through international exchange among teachers using ICT” – on intercultural competence for lesson study based on pre- and post-survey conducted for Japanese teachers.

In accordance with the GLS program, mathematics lesson studies on mathematics between Japanese and Singaporean elementary school teachers were conducted over a two-year period. Questionnaire surveys on intercultural competence for lesson study was conducted using 7-point Likert scale and descriptive questions with Japanese teachers (N = 5). Analysis of Wilcoxon's signed rank test and correlation analysis were conducted.

Followings are identified as the effects of GLS for Japanese teachers: (1) Japanese participants felt improvement of their competence in areas of attitude, internal outcomes and outward impact. (2) Participating in a GLS led to the improvement of Japanese teachers' qualities and abilities for subject teaching related to mathematics education. (3) Improvement of intercultural competence for lesson study was related to each other, and GLS led to improvement of these competences. (4) It was meaningful to have a two-year period to assimilate and adjust to independent experience of the host and guest.

The GLS led to teacher empowerment among this sample, not only as cooperative research, but also at the individual level of teachers where participants continued to develop lessons based on the GLS learning after study completion. This has important implications for the implementation and dissemination of the GLS.

This paper aims to deal with the selection of the sustainability-oriented Green Lean Six Sigma (GLS) project for the manufacturing industry in the complex decision-making environment. Moreover, the study also proposes a GLS implementation framework for improved organizational performance.

GLS project selection has been done based on the six sustainability-oriented criteria formed from 17 sub-criteria (found from the literature and developed by authors). The weights of the criteria have been determined through the entropy method. The projects have been ranked based on the criteria through the advanced decision-making approach: grey relation analysis (GRA). The results of the study were validated using best worst method and sensitivity analysis.

It has been found that the productivity-related criterion is the most significant among other criteria with entropy weight of 0.2721. GRA has been used in this research work to rank the potential GLS projects in a manufacturing industry based on six sustainability criteria, to select a project that exhibits the maximum potential for sustainable improvement. The machine shop has been found as the most significant GLS project with grey relation grade of 0.4742.

The present study facilitates practitioners and industrial managers to implement an inclusive GLS approach for improved sustainability dynamics through effective GLS project selection and implementation framework.

With increased globalized competition in recent times, new projects are being considered as the foundation stone for organizational success. The decision-making becomes quite complex to select an effective project due to the intriguing nature of various criteria, sub-criteria and different aspects of sustainability. To the best of the authors’ knowledge, the present study is the first of its kind that provides ways for the selection of sustainability-oriented GLS projects.

The purpose of this paper is to demonstrate with real data the enhanced statistical power of a GLS‐based event study methodology that requires the same input data as the traditional tests.

The paper uses full sample, subsample and simulated modified sample analyses to compare the statistical power of the GLS methodology with traditional methods.

The paper finds that it is often the case that traditional tests will not reject the null when a GLS‐based test may (strongly) reject the null. The power of the former is poor.

There are many published event studies where the null is not rejected. This may be because of the phenomenon being tested but it may also be because of the lack of power of traditional estimators. Hence, rerunning them with the authors' more powerful test is likely to reject some currently well‐accepted null hypotheses of no event effect, stimulating new research ideas. Moreover, as individual stocks have become more volatile, the additional power of the authors' methodology to detect abnormal performance for recent and future events becomes even more important.

There are more than 500 event studies in the top finance journals, which can broadly be split into two subgroups: contemporaneous shocks like changes in regulation and non‐contemporaneous events like mergers. GLS contemporaneous modeling of covariances in the former showed little efficiency gains. The paper's GLS modeling of variances for the latter demonstrates potentially huge effects. Practitioners should be skeptical of prior results accepting the null of no event effect and incorporate GLS to be confident of their future findings.

Guest lecturers (GLs) are a popular mechanism for transferring insights from industry into supply chain programmes. With a scarcity of capable supply chain (SC) talent in today's labour market, there is real value in executives contributing to SC programmes at schools and universities. By guest lecturing they can “light the fire” in students about how exciting the field and practice are and how much potential there is in considering a career in SC management. Students on SC programmes can be shown part of the way towards modern SC practice for which there is a real need for more talent. However, it may be difficult to ensure GL effectiveness; it is neither the executive's job nor his/her profession and academics often find it hard to recruit GLs.

Based on the hands‐on experience of GLs and academics from multiple backgrounds, this paper develops both a framework that can help ensure effective use of the GL, and 11 specific tips and suggestions for recruiting and using GLs in SC programmes.

It was found that there are more creative ways to use GLs in programmes than typically considered, as well as a wider recruitment of different types of GL than those most commonly sought after and most hard to recruit.

This paper aims to assist educators in curriculum development and to help to ensure that, when executives offer up scarce time and experience, they maximise the impact of their service.

The purpose of this exploratory study was to develop Global Lesson Study (GLS) defined as an international collaborative lesson study through international exchange of teachers using ICT. Its purpose is to nurture teachers from different countries with intercultural competence to conduct lesson study.

We developed an initial program for GLS in the subject of mathematics education between elementary school teachers in Japan and Singapore. The qualitative analysis of activities at each stage of the Pilot GLS was conducted from two perspectives: (1) intercultural competence for lesson study and (2) teacher's competency for subject instruction.

Through GLS, a new lesson was created that was only possible with discussions from teachers from different locations. It was clarified that GLS was not only useful for training teachers with intercultural competence for lesson study but also has led to the improvement of teacher's competency for subject instruction in mathematics.

The GLS is a new attempt in the sense of developing a high-quality lesson study method for creating new lessons as well as improving qualities and abilities of teachers through international exchange.

This paper studies test of hypotheses for the slope parameter in a linear time trend panel data model with serially correlated error component disturbances. We propose a test statistic that uses a bias corrected estimator of the serial correlation parameter. The proposed test statistic which is based on the corresponding fixed effects feasible generalized least squares (FE-FGLS) estimator of the slope parameter has the standard normal limiting distribution which is valid whether the remainder error is I(0) or I(1). This performs well in Monte Carlo experiments and is recommended.

This study aims to describe how a work team adapted to its fluctuated and severe environment by changing from “lean” to “over-lean” mode. To do this, the author investigated the relations among productivity, the vertical division of labor, and group leaders' behavior in a Japanese automobile assembly plant.

The authors conducted field study at an assembly plant for five months. They collected three plant-level data to investigate the capability of its shop floor: transition of production volume; transition of the number of workers; and productivity. And they collected two types of workforce data: skill map and work shift. Moreover, they videotaped the behavior of group leaders on several days and analyzed them through a time study.

The work team of this study achieved high productivity even in its tough environment. However, the authors' time study of group leaders showed that the group leaders, who usually engage in some management activities outside of the production line, did many tasks within the line. This indicates the team had a weakness toward the change of team members. Changing to this over-lean mode enabled the team to survive in a short-run, but maintaining the mode has a weakness in enhancing long-term competitiveness.

This study proposes a balance between the two modes is required for organizations if they are to survive their severe and fluctuating environments.

In the spirit of White’s (1982) paper, this paper examines the consequences of model misspecification using a panel data regression model. Maximum likelihood, random and fixed effects estimators are compared using Monte Carlo experiments under normality of the disturbances but with a possibly misspecified variance-covariance matrix. We show that the correct GLS (ML) procedure is always the best according to MSE performance, but the researcher does not have perfect foresight on the true form of the variance covariance matrix. In this case, we show that a pretest estimator is a viable alternative given that its performance is a close second to correct GLS (ML) whether the true specification is a two-way, a one-way error component model or a pooled regression model. Incorrect GLS, ML or fixed effects estimators may lead to a big loss in MSE.