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Questionnaire surveys made at currency markets around the world reveal that currency trade to a large extent not only is determined by an economy's performance or expected performance. Indeed, a fraction is guided by technical trading, which means that past exchange rates are assumed to provide information about future exchange rate movements. The purpose of this paper is to ask how a successful monetary policy should be designed when technical trading in the form of trend following is used in currency trading.

The paper embeds an optimal policy rule into Galí and Monacelli's dynamic stochastic general equilibrium (DSGE) model for a small open economy, which is augmented with trend following in currency trading, to examine the prerequisites for a successful monetary policy. Specifically, the conditions for a determinate rational expectations equilibrium (REE) that also is stable under least squares learning are in focus. The paper also computes impulse‐response functions for key variables to study how the economy returns to steady state after being hit by a shock.

The paper finds that a determinate REE that also is stable under least squares learning often is the outcome when there is a limited amount of trend following in currency trading, but that a more flexible inflation rate targeting in monetary policy sometimes cause an indeterminate REE in the economy. Thus, strict, or almost strict, inflation rate targeting in monetary policy is recommended also when there is technical trading in currency trading and not only when all currency trading is guided by fundamental analysis (in the form of rational expectations). This result is a new result in the literature.

There are already models in the literature on monetary policy design that incorporate technical trading in currency trading into an otherwise standard DSGE model. There is also a huge amount of DSGE models in the literature in which monetary policy is optimal. However, the model in this paper is the first model, to the best of the author's knowledge, where technical trading in currency trading and optimal monetary policy are combined in the same DSGE model.

This paper aims to examine whether external monitors (auditors and analysts) constrain earnings strings, an indicator of earnings management, and whether this monitoring is more effective after the implementation of the Sarbanes-Oxley Act of 2002 (SOX), given the emphasis of SOX on improving auditing, financial reporting and the information environment.

Agency theory establishes the premise between external monitoring and earnings strings. Auditor tenure and number of analysts following provide measures for external monitoring quality. Using prior research, empirical models explaining the presence of an earnings strings and earnings strings trend are developed to test the hypotheses.

Pre-SOX, extreme auditor tenure, indicating lower quality external monitoring, is associated with greater earnings strings trend, and analyst coverage is associated with increased likelihood of earnings strings and greater earnings strings trend consistent with analyst pressure on management. More effective auditor and analyst monitoring occurs post-SOX in terms of reduced likelihood of earnings strings and earnings strings trend.

The authors provide evidence on how elements of external monitoring are associated with increased earnings strings pre-SOX. Further, they contribute to the debate on the impact of SOX on external firm monitoring and the overall financial information environment. By focusing on earnings strings, the outcome of earnings management, the authors provide a unique understanding of external monitoring that also provides insight on the overvaluation of equity and ultimate destruction of firm value. The evidence demonstrates how regulation has contributed to an improved financial reporting environment and external monitoring.

A major challenge facing management in developed countries is improving the performance of knowledge and service workers, i.e. the decision and policy makers. In a developing country such as South Africa, with a well‐developed business sector, this need, especially in government, is even more crucial. South Africa has to face many new challenges in the 21st century ‐ growing environmental concerns, massive social and economic inequalities, high occurrences of HIV, low productivity, massive unemployment and the nation’s evolving role in Africa, amongst others. The importance of a sound science and technology policy framework to address these pressing issues cannot be overemphasised This paper discusses the construction of a knowledge‐base from a data repository concerning a South African National Research and Technology (NRT) Audit. This knowledge‐base is to be used as an aid when developing a science and technology policy framework for South Africa. The knowledge‐base is constructed using the cooperative inductive learning team (CILT) approach, which combines diverse data mining tools and human expertise into a cooperative learning system. In this approach, each data mining tool constructs a model of the knowledge as contained in the data repository, thus providing an automated tool to make sense of the knowledge embedded therein. That is, the data mining tools learn from the data in order to obtain new insights. The system also incorporates human domain expertise through the computational modelling of the human subject knowledge. The knowledge, as obtained during team learning, is stored in a team knowledge‐base. Results indicate that the CILT learning team approach can be successfully used to make sense of the vast amounts of data collected and provide a knowledge repository for further decision making and policy formulation.

The paper aims to present an integrated foresight framework and method to support decision-makers who are confronted with today’s complex and rapidly changing world. The method aims at reducing the degree of uncertainty by addressing the inertia or duration of unfolding trends and by placing individual trends in a broader context.

The paper presents a three-layered framework and method for assessing megatrends based on their inertia or duration. It suggests that if long-term trends and key future uncertainties are studied in conjunction at a meta-level and placed in a broader multi-layered framework of trends, it can result in new insights.

The application of the proposed foresight method helps to systematically place a wide range of unrelated trends and key uncertainties in the context of a broader framework of trends, thereby improving the ability to understand the inertia, direction and mutual interaction of these trends.

The elaboration of identified trends and key uncertainties is partly case-specific and subject to interpretation. It is aimed at illustrating the potential use of the framework.

The paper presents a new approach that may, by itself or in combination with existing foresight methods, offer new means for anticipating future developments.

The use of the proposed framework has potential to provide better insight in the complexity of today’s rapid-changing world and the major transitions taking place. It aims to result in sharper foresight by reducing epistemic uncertainty for decision-makers.

The paper demonstrates how megatrends, Kondratieff waves and century-long trends can be placed in an integrated framework and analysed in conjunction.

The purpose of this paper is to comprehensively review a large and heterogeneous body of academic literature on investors' feedback trading, one of the most popular trading patterns observed historically in financial markets. Specifically, the authors aim to synthesize the diverse theoretical approaches to feedback trading in order to provide a detailed discussion of its various determinants, and to systematically review the empirical literature across various asset classes to gauge whether their feedback trading entails discernible patterns and the determinants that motivate them.

Given the high degree of heterogeneity of both theoretical and empirical approaches, the authors adopt a semi-systematic type of approach to review the feedback trading literature, inspired by the RAMESES protocol for meta-narrative reviews. The final sample consists of 243 papers covering diverse asset classes, investor types and geographies.

The authors find feedback trading to be very widely observed over time and across markets internationally. Institutional investors engage in feedback trading in a herd-like manner, and most noticeably in small domestic stocks and emerging markets. Regulatory changes and financial crises affect the intensity of their feedback trades. Retail investors are mostly contrarian and underperform their institutional counterparts, while the latter's trades can be often motivated by market sentiment.

The authors provide a detailed overview of various possible theoretical determinants, both behavioural and non-behavioural, of feedback trading, as well as a comprehensive overview and synthesis of the empirical literature. The authors also propose a series of possible directions for future research.

The purpose of this paper is to examine the trend of intellectual capital disclosures (ICD) over a three‐year period (2008‐2010), when the Malaysian business environment was characterized by a number of major events such as the recent 2008/2009 global financial crisis and corporate governance restructuring.

A checklist was constructed to measure the extent and quality of ICD in Malaysian corporate annual reports. The extent of ICD was measured on a dichotomous basis (0, 1) while the quality of ICD was measured using a four‐point scale (0‐3).

The results showed an increasing trend of the ICD and a significant overall increase by the sample Malaysian companies. The results also revealed that there are significant differences between the categories of the IC disclosures, with external capital related information accounting for the largest portion. However, only human capital disclosures significantly increased over time.

The time series analysis carried out in this study observed that, despite the general ICD increasing trend, item‐specific analysis showed inconsistent results. Hence, the regulators may want to devise reporting guidelines for IC for Malaysian public listed companies.

This paper is one of the few which investigate trends in ICD following significant changes in the business environment in an attempt to determine if those changes have some impact on ICD practices.

The purpose of this study is to create a methodological approach for identifying priority areas for science and technology (S&T) development and its empirical application within the city of Moscow. This research uncovers a wide range of multicultural and multidisciplinary global trends that will affect the development of major cities in an era of complexity and uncertainty, including the inherent complexity of urban contexts, demographic and socioeconomic trends, as well as scientific and ecological factors.

The methodological approach is based on classic foresight instruments. Its novelty lays in the blending of qualitative and quantitative methods specially selected as the most appropriate for the identification of S&T areas in an era of complexity and uncertainty, including horizon scanning, bibliometric analysis, expert surveys and the construction of composite indexes with respect to the scope and resources of the research and the selected object for empirical application – Moscow, which is one of the world’s largest megacities. The analysis was performed for the period of 2009–2018 and expert procedures took place in 2019.

As a result, 25 global trends were identified, evaluated and discussed over the course of an expert survey and subsequent expert events. Ten priority areas of S&T development were determined, including 62 technological sub-areas within them and the most important market niches for all identified technological sub-areas, which could be useful for the world’s megacities. The results of this study are illustrated using the construction sector. Based on the conducted research and results, a list of recommendations on S&T policy measures and instruments were suggested, including the creation of the Moscow Innovation Cluster, which by the end of 2023 contained more than 6,000 projects and initiatives, selected using the findings of this investigation.

This research contributes to the existing literature and research agenda of setting priorities for S&T development and shows how it can be done for a megacity. The blended foresight methodology that was created within the study satisfies the criteria of scientific originality, is repeatable for any interested researcher, is applicable to any other city in the world and demonstrates its high efficiency in empirical application. It could be used for creating new agenda items in S&T policy, setting S&T priorities for a megacity and integrating the results into decision-making processes. This study provides recommendations on the further implementation of the designed methodology and results into a policymaking system. Moreover, the example of the Moscow Innovation Cluster, which was created based on the results of our research, demonstrates these recommendations’ practical significance in real life, which is quite valuable. The limitation of this study is that it is not devoted to urban planning issues directly or the promotion of R&D areas; it is about setting promising S&T priorities in an era of complexity and uncertainty for megacities.

The purpose of this study is to identify how themes and contributions featured in the four scholarly journals sponsored by the largest human resource development (HRD) research association (the Academy of Human Resource Development, AHRD) reflect the changing identity of the HRD field.

A frequency and content analysis of articles published during the period 2002-2011 was conducted to identify the dominant themes and research trend. Further, comments were made on the aims and scope and editorial discretion for each journal to understand how the journals influence the direction of scholarship in HRD.

It was found that the boundaries of the field are constantly expanding with some of the older and mature themes losing momentum and new themes coming to the forefront of scholarly interest. The journals were found to play a critical role in setting the future direction for the field.

Future researchers can examine if the waxing and waning themes identified in the findings remain same after analyzing contributions featured in journals that are not sponsored by the AHRD, but publish articles on topics closely related to HRD. Also, the findings can guide further examination of the editors’ leadership role in driving the evolution of the HRD field.

Considering the characteristics of HRD as an applied discipline, the findings can guide future researchers to explore if the thematic changes as identified in the study are associated with the needs of HRD practice.

The study attempts to understand the landscape of HRD research by looking at how the field’s identity boundaries have shifted over time and how different entities, like authors and editors publishing scholarly articles in the four HRD journals in the past decade, have interacted to contribute to the shift.

According to the perspective of evolutionary economic theory, the marketplace continuously evolves over time, following the changing needs of both customers and firms. In accordance with the theory, the second-hand apparel market has been rapidly expanding by meeting consumers’ diverse preferences and promoting sustainability since 2014. To understand what changes in consumers’ consumption behaviors regarding used apparel have driven this growth, the purpose of this study is to examine how the second-hand apparel market product types, distribution channels and consumers’ motives have changed over the past five years.

This study collected big data from Google through Textom software by extracting all Web-exposed text in 2014, and again in 2019, that contained the keyword “second-hand apparel,” and used the Node XL program to visualize the network patterns of these words through the semantic network analysis.

The results indicate that the second-hand apparel market has evolved with various changes over the past five years in terms of consumer motives, product types and distribution channels.

This study provides a comprehensive understanding of the changing demands of consumers toward used apparel over the past five years, providing insights for retailers as well as future research in this subject area.