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This study aims to develop a self-reflection scale useful for teachers to improve their skills and to clarify the Japanese teachers’ characteristics during mathematics lesson observation (MLO). In MLO, it is important to understand the lesson plan in advance to clarify observation points, and we aim to develop a scale including these points.

Based on the pre-questionnaire survey, nine perspectives and two situations for MLO were extracted. From these, a questionnaire for MLO was created. The results obtained from 161 teachers were examined, and exploratory factor analysis was conducted. ANOVA was conducted to analyze the effect of differences across the duration of teaching experience on the identified factors.

We developed a self-reflection scale consisting of 14 items with three factors: [B1] focus on instructional techniques and evaluation, [B2] focus on proactive problem-solving lesson development and [B3] focus on the mathematical background of the learning content. While duration of teaching experience showed no effect, three factors of the self-reflection scale for MLO showed a significant effect. Further multiple comparisons revealed the degree of focus was [B2]>[B1]>[B3].

Teachers who use this developed scale may grasp the strengths and weaknesses of their own MLO, which leads to self-improvement. The perspectives emphasized in lesson observation are the same when creating lesson plans and implementing lessons, leading to lesson improvement. Furthermore, based on the characteristics of teachers revealed, new training programs regarding MLO can lead to higher-quality lesson studies.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

This review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.

This review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.

The review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.

The review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.

The apparel industry of Bangladesh is rethinking lean manufacturing (LM) deployment because of the challenges imposed by the COVID-19 pandemic. Due to COVID-19, LM implementation in the apparel industry has become more difficult. Thus, the purpose of this study is to explore the barriers to implementing LM practices in the apparel industry of Bangladesh in the context of COVID-19 pandemic.

For evaluating the barriers, an integrated framework that combines the Delphi method and fuzzy total interpretive structural modeling (TISM) has been designed. The application of fuzzy TISM has resulted in a structured hierarchical relationship model of the barriers with driving and driven power.

The findings reveal that “lack of synchronization of lean planning with strategic planning”, “lack of proper understanding of lean concept” and “low priority from the top management” are the three top most important barriers of LM implementation in apparel industry.

These findings will help the apparel industry to formulate strategy for implementing the LM practices successfully. The proposed model is expected to contribute to the sustainable development goals (SDGs) such as Responsible Consumption and Production (SDG 12); Decent Work and Economic Growth (SDG 8); Industry, Innovation and Infrastructure (SDG 9) via resilient strategies.

This study is one of few initial efforts to investigate LM implementation barriers during the COVID-19 epidemic in a real-world setting.

The apparel and textile sector poses a significant environmental challenge due to its substantial contribution to pollution in the form of air, water and soil pollution. To combat these issues, the adoption of sustainable practices is essential. This study aims to identify and analyse the barriers that hinder the progress of sustainability in the apparel and textile industry. By consulting experts in the field, critical barriers were identified and given special attention.

To achieve the research objective, an integrated approach involving Interpretive Structural Modelling (ISM) and fuzzy MICMAC decision-making techniques was employed. The results were further validated through the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method.

The findings highlight that barrier related to clothing disposal, inadequate adaptation to modern technology, challenges affecting sector efficiency and issues related to fashion design are crucial in influencing the remaining six barriers. Based on the outcomes of the DEMATEL method, a comprehensive cause-and-effect diagram was constructed to gain a deeper understanding of these challenges.

This research provides valuable insights for policymakers and stakeholders in the apparel and textile industry. It offers a strategic framework to address and overcome sustainability barriers, promoting the development of a more environmentally responsible and resilient sector.

The purpose of this research is to conduct an in-depth investigation of the barriers apparel and textile sectors. It is feasible that both the management team and the medical experts who provide direct patient care could benefit from this research.

The COVID-19 outbreak disrupted the business environment and severely affected the morale and performance of the employees. Further, the Indian automobile industry witnessed major setbacks and drastically impacted sector in COVID-19. Talent agility is an emerging concept in the field of HRM that will foster innovations and productivity in the automobile industry. Thus, this study aims to explore the barriers to building in-house agile talents in the Indian automobile industry in the new normal.

The barriers of talent agility were identified through a literature review and validated through experts’ opinions. This study used a hybrid approach, which combines Interpretive Structural Modelling-Polarity (ISM-P) and decision-making trial and evaluation laboratory (DEMATEL) to develop a hierarchical structural model of the barriers, followed by classification into cause and effect groups.

The result of the multi-method approach identified that shortage of skills and competencies, lack of IT infrastructure, lack of ambidextrous leaders, lack of smart HRM technologies and practices, lack of attractive reward system/career management, poor advanced T&D, poor industry, institute interface and financial constraints are the critical barriers.

It can provide a strategic roadmap for automobile manufacturers to promote talent agility in the current wave of digitalization (Industry 4.0). This study can help the managers to address and overcome the barrier and hurdles in building talent agility.

This study is unique in that it addresses the contemporary issues related to talent agility in the context of the Indian automobile industry in the current rapidly changing environment. This study developed a holistic integrated ISM(P)-DEMATEL hierarchical framework on the barriers of talent agility indicating inner dependency weights, i.e., the strength of interrelationship between the barriers.

We wanted to find out how infant multinationals originating from Poland enact opportunities in Sub-Saharan African (SSA) countries.

We conducted a comparative case study of four Polish firms operating in SSA.

We found that when entering SSA, studied firms employed effectual decision-making logic. Thus, their internationalization was means-driven, serendipitous, partnership-oriented, based on the “affordable loss” principle and focused on shaping opportunities in SSA, rather than predicting, analyzing and planning any firm-specific assets or capabilities.

We illuminated the nature of the means employed in effectual internationalization and the role of partners (“effectual stakeholders”) in this process. Thus, we contribute to a deeper understanding of how infant multinationals navigate extreme uncertainty in the emerging SSA markets.

We form portfolios based on return and liquidity and examine the effects of liquidity and other risk factors on asset pricing in the Chinese stock market. Our results show that the past loser-and-illiquid stock portfolios tend to outperform the past winner-and-liquid stock portfolios in the 1–12 months holding period. The excess return is significantly associated with the market-wide liquidity factor even when we control the three Fama-French and momentum factors. Cross-sectionally, the liquidity beta significantly affects the excess return even with control of other risk betas and other traditional liquidity proxies.

There is growing recognition that effective project control systems (PCS) are critical to the success of projects. The relationship between the individual elements of PCS and successfully achieving project objectives has yet to be explored. This research investigates the enablers and barriers that influence the elements of PCS success and drive project objectives.

This study adopts a mixed approach of descriptive analysis and regression models to explore the impact of six PCS elements on project outcomes. Petroleum and chemical projects in Saudi Arabia were selected as a case study to validate the research model.

Data from a survey of 400 project managers in Saudi’s petroleum and chemical industry reveal that successful PCS are the key to achieving all project outcomes, but they are particularly critical for meeting project cost objectives. Project Governance was identified as the most important of the six PCS elements for meeting project objectives. A lack of standard processes emerged as the most significant barrier to achieving effective project governance, while having skilled and experienced project team members was the most significant enabler for implementing earned value.

The study offers a direction for implementing and developing PCS as a strategic tool and focuses on the PCS elements that can improve project outcomes.

This research contributes to project management knowledge and differs from previous attempts in two ways. Firstly, it investigates the elements of PCS that are critical to achieving project scope, schedule and cost objectives; secondly, enablers and barriers of PCS success are examined to see how they influence each element independently.

Drawing upon social comparison theory, this paper explores the (in)congruence effect of leader and follower overqualification on leader's downward envy, which in turn leads to leader undermining behavior.

Using two-wave, multi-sourced data gathered in China, a polynomial regression was conducted on 301 leader-follower dyads.

Results show asymmetrical incongruence effects, indicating greater leader's downward envy when leader overqualification was lower than follower overqualification. In addition, by increasing downward envy, leader-follower (in)congruence in overqualification has an indirect positive effect on leader undermining behavior.

This study highlights the importance of being conscious of both parties’ overqualification levels to avoid unfavorable outcomes. Meanwhile, training for both parties is crucial, offering a holistic understanding of leader-follower overqualification differences and downward envy, as well as skills to manage “triggers” of leader undermining.

Our study is among the first to examine the effects of overqualification from a leader-follower dyadic congruence perspective. It suggests that leader's downward envy and undermining behaviors toward followers are influenced by both follower and leader overqualification.