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This study aims to examine to what extent the Japanese financial markets are affected by the four periods of unconventional monetary policies (UMP) implemented by the Bank of Japan from 2013 to 2020.

Using the daily 10-year term spread as a proxy for monetary easing policy, this study uses four sub-sample periods from 2013 to 2020 to look into the effectiveness of UMP on the Japanese financial markets.

Our result shows that not all of the Bank of Japan's unconventional monetary policies are equally effective in influencing the Japanese financial markets. In particular, the QQE policy implemented from April 2013 to October 2014 effectively influenced the stock market, banking sector and foreign exchange market. However, the financial market impact of monetary policy is muted during the QQE expansion period. Likewise, the QQE with a negative interest rate policy influences only the banking sector. Finally, the QQE with its yield curve control policy effectively impacts the financial markets.

This research can be expanded by studying the international spillover effect of the Bank of Japan's UMP on the financial markets in Asian countries.

The findings of this study enable investors to understand the causal relationship between the Bank of Japan's UMP and the financial market indicators, thereby helping them to position their portfolio investments. From the policy perspective, the finding is useful to inform the Bank of Japan on which policy is relatively effective in affecting the financial markets. In light of the empirical finding, the Bank of Japan should continue to pursue the QQE YCCP or revert to the initial QQE policy, as the two policies are relatively more effective than the QQE expansion and QQE NIRP in affecting the Japanese financial markets.

The empirical finding highlights the importance of controlling for the impact of different QQE policies in the model. Future research may consider conducting sub-sample analysis to cater to the different QQE policy regimes. This approach provides a clearer picture and valid inferences on the financial market impact of each QQE policy.

This study provides a comprehensive analysis of the impact of Bank of Japan's QQE on the Japanese financial markets. For the market participants, the findings of this study suggest that investors should closely gauge the development of the unconventional monetary policies of the Bank of Japan because the monetary easing policy influences the decision-making process of commercial banks, pension funds, mutual funds, retail investors and other stakeholders in the financial markets. The policy twist will have future ramifications for their loan, investment and retirement fund portfolios.

In order to ensure effectiveness of staff's performance using online meetings applications during coronavirus disease (COVID-19), having the behavioural intention is mandatory for staff to measure, test, and manage the staff's data. Understanding of Public Higher Education Institution (PHEI) staffs' intention and behaviour toward online meetings platforms is needed to develop and implement effective and efficient strategies. The objectives of this paper to identify the factors that affect staff to use online meetings applications, to develop a model that examining the factors that affect PHEI staff to online meetings applications and to validate the proposed model. This study used a cross-sectional quantitative correlational study with using UTAUT2 model by validating the model and mediating variables to enhance the model's explanatory power and to make the model more applicable to PHEI staff's behavioural intention.

The data were collected in Malaysia from March to May 2021. The survey took place using Google form and was send to PHEI staff for answer. This research particularly chooses PHEI as the location to carry out the research due to two main factors. Statistical analysis and hypotheses were tested using structural equation modelling based on the optimisation technique of partial least squares. SmartPLS software, Version 3.0 (Hair et al., 2010) was used to conduct the analysis. A conceptualised estimation model was “drawn in” the partial least squares structural equation modeling (PLS-SEM) to analyse the consequences of the variables' relationships. In essence, the PLS-SEM simulation was carried out in a model by assessing and computing various parameters that included elements like validity, durability, and item loading. Henseler et al. (2009) suggested a two-step method that includes PLS model parameter computing. This is accomplished by first solving the estimation model in the structural model independently before calculating the direction coefficients. The results of data analysis using SmartPLS findings and interpretation of the data are addressed. The questionnaire was extensively examined to ensure that the data obtained were presented in a clear and intelligible manner, with the use of figures, and graphs.

This current study found that the usability of the material, the reliability of operating, the impact of the PHEI staff's views on its usage, and finally the familiarity with the online meetings platforms influenced PHEI staff's behavioural intention for adoption and long-term use of online meeting platforms using UTAUT2. The staff's behavioural intention for using online meeting platforms was significantly influenced by the effort expectancy, facilitating conditions and habit of online meeting platforms. There was a clear association between “Habit” and “Behavioural Intention” for the usage of information technology in learning in several studies (El-Masri and Tarhini, 2017; Uur and Turan, 2018; Mosunmola et al., 2018; Venkatesh et al., 2003). As a consequence of the utility of online meeting platforms in daily staff meetings and learning activities, this technology has been adopted.

This study used UTAUT2 and structural equations modelling in this study to assess respondents' perspectives on the use of online meetings platforms in PHEI, since users' perspective is a significant factor in the adoption and acceptance of online meeting applications. Staff's behavioural intention to use online meeting platforms was effectively enhanced by “Effort Expectancy,” “Facilitating Conditions” and “Habit” in this study. The study shows that identifying PHEI staff's perspectives will effectively increase the staff's aversion to utilising online meeting platforms for online meetings purposes.

The purpose of this study is to analyse the impact of the European Central Bank’s (ECB) Public Sector Purchase Programme (PSPP) on Spanish sovereign debt.

The authors assess the impact of the PSPP on Spanish Government bonds from two different transmission channels (the signalling and the portfolio substitution) with two effects for each of them (the announcement and the expectation effects for the former and the stock and the rebalancing effects for the latter). The empirical study has been undertaken with event study methodology, controlled by macroeconomic variables, panel data and cross-sectional regression analyses.

The results show that both the ECB’s purchases under the PSPP and the announcements reduced Spanish Government bond yields. Compared to previous literature the Spanish Government bond yields reductions are larger than those for other countries.

The authors’ approach to the impact of investors’ expectations is interesting, although they cannot draw evidence on this issue due to the lack of data.

From an economic perspective, the ECB can change economic agents’ expectations without actually carrying out any programme, only by announcing such a programme.

This paper contributes to the literature examining the PSPP from different transmission channels in Spain, taking into account the announcements, the expectations, the purchases and the variation in debt holdings relating to the PSPP from the beginning of the programme until 2020. Due to the large degree of heterogeneity across euro area countries, the results in this paper should improve our understanding of the relative differences in the impact of the PSPP and, thus, be of interest to academics and policymakers.

Judging bias is ironically an inherent risk in every competition, which might threaten the fairness and legitimacy of the competition. The patriotism effect represents one source of judging bias as the judge favors contestants who share the same sentiments, such as the nationalistic, racial, or cultural aspects. This study attempts to explore this type of judging bias in a university student competition. In addition, this study tries to expand the literature on judging bias by proposing the term universitarian bias as the judge is suspected to give a higher score to contestants from the same university.

The association rule of data mining is used to accomplish the objective of the study. To demonstrate the applicability of the method, the data set from the annual national university student competition in Indonesia is exploited.

The result strongly discovers that the universitarian bias is likely to be present. Some recommendations are also provided in order to minimize the bias that might happen again in the future.

As the implication of the presence of the universitarian bias, the committee should remove all the university features attributed to the participants. This endeavor is expected to minimize the universitarian bias that might happen.

This research is claimed to be the first attempt in implementing the data mining technique in the field of judging bias.

Various approaches have emerged to assist practitioners in making more informed decisions in highway construction projects. However, industry practitioners are still using subjective ways to make decisions. Also, researchers have developed tools and techniques with similar objectives. Lack of information on what has been developed might lead to those issues. Therefore, this paper aims to review trends of evolution, pinpoint strengths and gaps in the literature and identifies potential future directions for decision-making research in highway construction projects.

A systematic review was conducted on published articles on decision-making in highway construction projects using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique.

The analysis of 101 articles revealed that existing decision-making research in highway construction projects targets improvements in four areas: feasibility, conceptual, detailed scope and detailed design. The four areas consist of sixteen subthemes that are detailed in this study. In addition, most research involved developing decision support tools and systems as well as decision-making models, techniques and frameworks. Lastly, several research areas have emerged, such as adding more decision criteria including those with uncertainties, expanding existing decision-making models into decision support systems, benchmarking decision criteria between different sample populations and exploring inter-and intra-relationships between decision criteria.

This paper provides an overview of existing research on decision-making in highway construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research. Finally, industry practitioners can use the findings to develop strategies for effective decision-making processes.

The purpose of this study is to review university social responsibility (USR) using a well-known quantitative approach of bibliometric analysis. Compared to corporate social responsibility (CSR), USR is in its infancy stage, requiring further exploration of its meaning and impact toward higher education institutions (HEIs).

A total of 306 publications and 9,530 cited references were retrieved from the Web of Science database. Bibliographic coupling analysis was applied to uncover present themes, while co-word analysis was used to predict future trends.

Findings suggested that themes are centralized toward the impact of USR on HEIs. USR is important for HEIs to attain excellence in today's challenging business environment.

USR could develop HEIs into world-renowned institutions by establishing a good reputation and engaging with the broader community, locally and internationally. USR is regarded as HEIs' new value proposition in marketing its brand within the competitive environment in higher education.

This study is the first that reviews USR by extracting the knowledge structure and providing visualization on the implicit themes of the subject interest.

Gender bias and stereotypes are universal issues in today’s society. This study presents a comprehensive review of women’s leadership based on bibliometric analysis. The feminist approach to leadership is helpful in many ways, and it could be the type of leadership needed in the challenging world of academia. Women present unique characteristics and traits, particularly their motherly approach to leadership.

A review of the literature based on bibliometric analysis by mapping the knowledge structure of the subject is conferred by uncovering the past, present and future trends based on three bibliometric analyses.

The finding suggests that gender equality, stereotypes and barriers in women’s leadership are at the forefront of the subject in HEIs. The overall system, mindset and willingness for institutional transformation are needed to change the mindset of accepting women as leaders of HEIs.

This study brought the significant idea of increasing women’s empowerment in HEIs, eventually strengthening institutional leadership’s capability towards advanced education.

This study would present a crucial foundation in women’s empowerment, particularly from the HEIs perspective and from the generic women’s leadership literature.

This study aims to provide an in-depth understanding of big data analytics (BDA) in human resource management (HRM). The emergence of digital technology and the availability of large volume, high velocity and a great variety of data has forced the HRM to adopt the BDA in managing the workforce.

This paper evaluates the past, present and future trends of HRM through the bibliometric analysis of citation, co-citation and co-word analysis.

Findings from the analysis present significant research clusters that imply the knowledge structure and mapping of research streams in HRM. Challenges in BDA application and firm performances appear in all three bibliometric analyses, indicating this subject’s past, current and future trends in HRM.

Implications on the HRM landscape include fostering a data-driven culture in the workplace to reap the potential benefits of BDA. Firms must strategically adapt BDA as a change management initiative to transform the traditional way of managing the workforce toward adapting BDA as analytical tool in HRM decision-making.

This study presents past, present and future trends in BDA knowledge structure in human resources management.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

This study aims to examine the performance indicators (PIs) for assessing environmental management plan (EMP) implementation in road construction projects. The specific objectives are to compare the key PIs between environment auditors and environment officers and among project stakeholders, develop components to categorize interrelated key PIs and evaluate the effectiveness of interrelated key PIs and components.

Thirty-nine PIs were identified through a systematic literature review and in-depth interviews with environmental professionals. Subsequently, a questionnaire survey was designed based on this list of PIs and distributed to industry professionals. Sixty-one responses were collected in Malaysia and analyzed using the mean score ranking, normalization, agreement analysis, overlap analysis, factor analysis and fuzzy synthetic evaluation.

The analyses identified 18 key PIs: soil erosion, dust appearance, spill of chemical substance, construction waste, clogged drainage, overflowed silt trap, oil/fuel spills, changes in the colour of bodies of water, excessive cut and fill, vegetation depletion, changes in the colour of the runoff water, landslide occurrence, slope failures, irregular flood, public safety, deforestation, open burning and increased of schedule waste. Also, the key PIs can be grouped and ranked into the following four components: geological, pollution, environmental changes and ecological. Finally, the overall importance of the key PIs is between important and very important.

This study is a pioneer in quantitively examining the key PIs for EMP implementation in road construction projects. Researchers, industry practitioners and policymakers can use the findings to develop strategies and tools to allow public monitoring of EMP implementation.