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This paper aims to examine the return and volatility transmission among economic policy uncertainty (EPU), geopolitical risk (GPR), their interaction (EPGR) and five tradable precious metals: gold, silver, platinum, palladium and rhodium.

Applying time-varying parameter vector autoregression (TVP-VAR) frequency-based connectedness approach to a data set spanning from January 1997 to February 2023, the study analyzes return and volatility connectedness separately, providing insights into how the data, in return and volatility forms, differ across time and frequency.

The results of the return connectedness show that gold, palladium and silver are affected more by EPU in the short term, while all precious metals are influenced by GPR in the short term. EPGR exhibits strong contributions to the system due to its elevated levels of policy uncertainty and extreme global risks. Palladium shows the highest reaction to EPGR, while silver shows the lowest. Return spillovers are generally time-varying and spike during critical global events. The volatility connectedness is long-term driven, suggesting that uncertainty and risk factors influence market participants’ long-term expectations. Notable peaks in total connectedness occurred during the Global Financial Crisis and the COVID-19 pandemic, with the latter being the highest.

Using the recently updated news-based uncertainty indicators, the study examines the time and frequency connectedness between key uncertainty measures and precious metals in their returns and volatility forms using the TVP-VAR frequency-based connectedness approach.

This study aims to convey lessons learned from two sustainability initiatives at Norway’s largest university. This contributes to knowledge-based discussions of how future, sustainable higher education institutions (HEIs) infrastructures should be envisioned and planned if the fundamental uncertainty of the future development of learning, researching and teaching is acknowledged.

This study was submitted on 24 January 2023 and revised on 14 September 2023. HEIs, particularly when they are engaged in research activities, have a considerable environmental footprint. At the same time, HEIs are the main producers and disseminators of knowledge about environmental challenges and their employees have a high awareness of the urgent need to mitigate climate change and biodiversity loss. In this study, the gap between knowledge and environmental performance is addressed as a question of infrastructural change, which is explored in two case studies.

The first case study presents limitations of ambitious, top-down sustainability planning for HEI infrastructures: support from employees and political support are central for this strategy to succeed, but both could not be secured in the case presented leading to an abandonment of all sustainability ambitions. The second case study exposes important limitations of a circular approach: regulatory and legal barriers were found against a rapid and radical circular transformation, but also more fundamental factors such as the rationality of an institutional response to uncertainty by rapid cycles of discarding the old and investing in new equipment and facilities.

Being based on qualitative methods, the case studies do not claim representativity for HEIs worldwide or even in Norway. Many of the factors described are contingent on their specific context. The goal, instead, is to contribute to learning by presenting an in-depth and context-sensitive report on obstacles encountered in two major sustainability initiatives.

Research reporting on sustainability initiatives too often focuses descriptively on the plans or reports the successes while downplaying problems and failures. This study deviates from this widespread practice by analysing reasons for failure informed by a theoretical frame (infrastructural change). Moreover, the juxtaposition of two cases within the same context shows the strengths and weaknesses of different approaches to infrastructural change particularly clearly.

This study aims to analyse the effects of Economic Policy Uncertainty (EPU) on the return of growth/value and small/large-cap stocks during expansionary and recessionary periods across a conditional distribution.

The authors selected a sample covering the period between 01/1995–05/2021. Quantile regressions were applied to the EPU and Russell indices. Business cycles were established following the NBER.

The results show that EPU has a negative effect on stocks with the intensity of the effect depending on the stock's profile. Small-cap and growth stocks were found to be most sensitive to EPU, especially during recessions. The negative effect is moderated by the economic cycle but is progressively diluted at the lower tail of the stock return distribution.

The findings shed more light on investment strategies for growth/value investors that pursue opportunities arising from a changing economic cycle.

This study makes the following contributions: (1) explores the impact of EPU on the return of different stocks across a conditional distribution, and (2) provides evidence on how the economic cycle influences EPU impact on growth/value stocks and small/large stocks.

:本研究擬分析跨條件分佈、以及於擴張期和衰退期,經濟政策不確定性對成長型股票/價值股和小盤股/大型股的收益的影響。

我們選擇了涵蓋1995年1月與2021年5月期間的樣本進行研究。我們於經濟政策不確定性指數和羅素指數上採用分位數迴歸法進行研究; 並跟隨著美國國家經濟研究局,建立了多個經濟週期。

研究結果顯示,經濟政策不確定性對股票是有負面影響的,而影響的強度則視乎股票的投資組合而定。我們發現,小盤股和成長型股票對經濟政策不確定性是非常敏感的,尤其是在經濟衰退期間。這負面影響會被經濟週期緩和,唯這緩和作用卻會在股票收益的低尾處逐漸減輕。

研究結果使我們更容易理解為尋找因經濟週期改變而衍生的機會的增長/價值投資者所提供的投資策略。

本研究有以下的貢獻:(一) 、 探究了經濟政策不確定性對跨條件分佈、不同的股票收益的影響; (二) 、為經濟週期會如何左右經濟政策不確定性對成長型股票/價值股和小盤股/大型股的影響,提供了證據。

The purpose of this paper is to review and analyze assumptions and the appropriateness of the most dominant strategic decision-making theories within the Islamic cultural context as an attempt to develop an Islamic decision-making framework.

This paper adopted the integrative literature review approach as a research method (Torraco, 2005). This method allows the researcher to evaluate and syndicate the relevant literature to, critically, review and expand on the theoretical foundation of the topic and, hence, develop new theoretical perspectives and views.

Based on the critical review of the decision-making theories from an Islamic perspective, Islamic culture confirms the behavioural decision theory as the most appropriate approach to make strategic decisions in organizations. In addition, the study reveals that mutual consultation and consultative decision-making (Shura), based on knowledge and Islamic ethics, is the principal Islamic approach to strategic decision-making.

The developed Islamic decision-making framework will, significantly, assist management practitioners, managers and policy makers in both private and governmental organizations to improve their decision-making skills through adopting the Shura approach in decision-making.

The paper expands the boundaries of knowledge in managerial decision-making through developing an Islamic decision-making framework. This theoretical framework brings new insights and open new opportunities of thinking on Islamic decision-making among business scholars and represents fundamental grounds for future research in cross-cultural management in the area of managerial decision-making from an Islamic perspective, which is rare among scholars of management.

This paper aims to investigate the dynamic volatility connectedness among 13 G20 countries by using the volatility indices.

The connectedness approach based on the time-varying parameter vector autoregression model has been used to investigate the linkage. The period of study is from 1 January 2014 to 20 April 2023.

This analysis revealed that volatility connectedness among the countries during COVID-19 and Russia–Ukraine conflict had increased significantly. Furthermore, analysis has indicated that investors had not anticipated the World Health Organization announcement of COVID-19 as a global pandemic. Contrarily, investors had anticipated the Russian invasion of Ukraine, evident in a significant rise in volatility before and after the invasion. In addition, the transmission of volatility is from developed to developing countries. Developed countries are NET volatility transmitters, whereas developing countries are NET volatility receivers. Finally, the ordinary least square regression result suggests that the volatility connectedness index is informative of stock market dynamics.

The connectedness approach has been widely used to estimate the dynamic connectedness among market indices, cryptocurrencies, sectoral indices, enegy commodities and metals. To the best of the authors’ knowledge, none of the previous studies have directly used the volatility indices to measure the volatility connectedness. Hence, this study is the first of its kind that has used volatility indices to measure the volatility connectedness among the countries.

This study aims to investigate the dynamic relationships between profitability, credit risk, liquidity risk and capital in Indonesian banking industry.

The authors use a panel vector autoregression model that incorporates macroeconomic variables: growth, interest rate, foreign exchange. The analysis is based on a monthly panel data set of 88 banks spanning from January 2012 to September 2021, which comprises 10,296 bank-month observations.

Our key findings highlight (i) permanent credit cost and liquidity cost pass through practices, (ii) complementary function of liquidity and capital, (iii) earning management motivated asset write off and (iv) credit risk-liquidity risk neutrality. In addition, the authors observe that the banks demonstrated resilience to macroeconomic shocks.

Our study have shown some interesting dynamic patterns of fundamentals; nevertheless, unified theoretical underpinning of the process is still unavailable. This should be an important future reasearch avenue.

The study brings significant implications for regulatory and supervisory practices aimed at enhancing the financial stability of banks.

We conduct estimation of Indonesian banks system in dynamic perspective and perform impulses responses.

This paper aims to examine the key challenges experienced and lessons learned when organizations undergo large-scale agile transformations and seeks to answer the question of how incumbent firms achieve agility at scale.

Building on a case study of a multinational corporation seeking to scale up agility, the authors combined 36 semistructured interviews with secondary data from the organization to analyze its transformation since the early planning period.

The results show how incumbent firms develop and successfully integrate agility-enhancing capabilities to sense, seize and transform in times of digital transformation and rapid change. The findings highlight how agility can be established initially at the divisional level, namely with a key accelerator in the form of a center of competence, and later prepared to be scaled up across the organization. Moreover, the authors abstract and organize the findings according to the dynamic capabilities framework and offer propositions of how companies can achieve organizational agility by scaling up agility from a divisional to an organizational level.

Along with in-depth insights into agile transformations, this article provides practitioners with guidance for developing agility-enhancing capabilities within incumbent organizations and creating, scaling and managing agility across them.

Examining the case of a multinational corporation's exceptional, pioneering effort to scale agility, this article addresses the strategic importance of agility and explains how organizational agility can serve incumbent firms in industries characterized by uncertainty and intense competition.

The purpose of this study is to analyse and compile the literature on various option pricing models (OPM) or methodologies. The report highlights the gaps in the existing literature review and builds recommendations for potential scholars interested in the subject area.

In this study, the researchers used a systematic literature review procedure to collect data from Scopus. Bibliometric and structured network analyses were used to examine the bibliometric properties of 864 research documents.

As per the findings of the study, publication in the field has been increasing at a rate of 6% on average. This study also includes a list of the most influential and productive researchers, frequently used keywords and primary publications in this subject area. In particular, Thematic map and Sankey’s diagram for conceptual structure and for intellectual structure co-citation analysis and bibliographic coupling were used.

Based on the conclusion presented in this paper, there are several potential implications for research, practice and society.

This study provides useful insights for future research in the area of OPM in financial derivatives. Researchers can focus on impactful authors, significant work and productive countries and identify potential collaborators. The study also highlights the commonly used OPMs and emerging themes like machine learning and deep neural network models, which can inform practitioners about new developments in the field and guide the development of new models to address existing limitations.

The accurate pricing of financial derivatives has significant implications for society, as it can impact the stability of financial markets and the wider economy. The findings of this study, which identify the most commonly used OPMs and emerging themes, can help improve the accuracy of pricing and risk management in the financial derivatives sector, which can ultimately benefit society as a whole.

It is possibly the initial effort to consolidate the literature on calibration on option price by evaluating and analysing alternative OPM applied by researchers to guide future research in the right direction.

In the project initiation phase, an appraisal is needed to clarify the strategic problem and alternative solutions. Full-scale construction projects and simple solutions (do-minimum alternatives) should be assessed. The do-nothing alternative is the baseline for the appraisal and an option in itself. The paper explores the role of do-nothing and do-minimum alternatives in public project appraisal, which may significantly impact the attractiveness of a construction project.

The paper presents an empirical study from Norway, which requires external quality assurance (QA) of early project appraisals. The data include an extensive document review of 112 projects and interviews with 41 experts involved in the appraisal processes.

Of 112 appraisals, 110 recommended a major construction project, including cases where the benefits and value were low or uncertain. The do-nothing alternative was generally included as a reference but not treated as a viable option. Do-minimum alternatives were often not explored. By contrast, the external QA reports recommended do-nothing or do-minimum in 28 cases. Interestingly, although political decision-makers rarely reject projects, they may put them on hold indefinitely, implying that the actual outcome in many cases is still do-nothing.

The paper addresses a topic that has been understudied in the literature. The findings contribute to the broader literature on project initiation processes, project appraisal and how to reduce the risk of bias and manipulation in appraisals.

Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.

The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty.

It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice.

This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.