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The authors test the weak-form efficiency in cryptocurrency markets using the most recent and comprehensive data as of 2021. The authors apply various technical indicators to take a long or short position on 99 cryptocurrencies and compare the 10-day returns based on the technical trading strategies to the simple buy-and-hold returns. The authors find that the trading strategies based on single indicators or the combination of two indicators do not generate higher returns than buy-and-hold returns among cryptos. These findings suggest that cryptocurrency markets are weak-form efficient in general.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

In academia, assessment is often narrow in its focus on research productivity, its application of a limited number of standardised metrics and its summative approach aimed at selection. This approach, corresponding to an exclusive, subject-oriented concept of talent management, can be thought of as at odds with a broader view of the role of academic institutions as accelerating and improving science and scholarship and its societal impact. In recent years, open science practices as well as research integrity issues have increased awareness of the need for a more inclusive approach to assessment and talent management in academia, broadening assessment to reward the full spectrum of academic activities and, within that spectrum, deepening assessment by critically reflecting on the processes and indicators involved (both qualitative and quantitative). In terms of talent management, this would mean a move from research-focused assessment to assessment including all academic activities (including education, professional performance and leadership), a shift from focus on the individual to a focus on collaboration in teams (recognising contributions of both academic and support staff), increased attention for formative assessment and greater agency for those being evaluated, as well as around the data, tools and platforms used in assessment. Together, this represents a more inclusive, subject-oriented approach to talent management. Implementation of such changes requires involvement from university management, human resource management and academic and support staff at all career levels, and universities would benefit from participation in mutual learning initiatives currently taking shape in various regions of the world.

Knowledge-intensive entrepreneurial firms (KIE) strongly rely on scientific and strategic research and development (R&D) capabilities to achieve higher performance levels. Hence, the purpose of this paper is to disentangle the effects of scientific capabilities and strategic R&D on KIE performance; and how the constituent elements of these dimensions can be configured to generate conditions for high performance.

The authors’ empirical setting involves companies that submitted projects to the Innovative Research in Small Businesses (PIPE) program in Brazil. The authors then run partial least square structural equation modeling to verify how scientific and strategic R&D capabilities influence the performance construct. Second, the authors apply fuzzy-set qualitative comparative analysis to identify configurations that are equifinal in terms of generating superior performance.

Findings indicate a strong association between scientific capabilities and KIE performance. The configurational approach outlines the existence of multiple paths to success, but human capital stands as a core condition throughout estimations.

The authors’ assessment has implications for how KIE firms are managed according to their organizational profiles and trajectories. Also, it advances the authors’ comprehension on how entrepreneurship policies can better target these distinct profiles.

The authors’ analysis provides new evidence on the inherent complexity behind the generation of high performance in KIE when addressing their portfolios of knowledge-related capabilities. More than that, the authors were able to identify the existence of heterogeneous profiles that can equally lead to higher levels of performance.

The objective of the present study is to examine the impact of corporate characteristics on human resource disclosures in Indian corporate sector.

The study investigates the annual reports of 336 Indian listed companies of NSE-500 Index. The data are collected for the latest time period which contains eight years (FY 2012–13 to 2019–2020). The data of independent variables (company characteristics) have collected from annual reports and CMIE ProwessIQ Database of the Indian listed companies. The data of human resource dissclosure index (HRDI) is collected form annual reports using content analysis approach. For analysis purpose, descriptive statistics, Pearson's correlation matrix, Two-way Least Square Dummy Variable (LSDV) regression model have been used.

The outcomes show that net sales, market capitalisation, ROTA, return on equity, quick ratio, PAR have significant positive and age, profit after tax, current ratio have significant negative effect on HRDI. On the contrary, debt-equity ratio, earnings per share, type of auditor, listing status have insignificant positive and net fixed assets, promoter's holding have insignificant negative effect on HR disclosures of the selected Indian listed companies.

The HRDI constructed in the present study helps the Institute of Chartered Accountants of India (ICAI) and other regulatory bodies to make some standards regarding voluntary HR disclosure practices in Indian corporate sector.

Crowdfunding (CF) has become an increasingly popular means of financing for entrepreneurs and has attracted significant attention from both researchers and practitioners in recent years. The purpose of this study is to investigate the core content and knowledge diffusion paths in the CF field. Specifically, we aim to identify the main topics and themes that have emerged in this field and to trace the evolution of CF knowledge over time.

This study employs co-word clustering and main path analysis (MPA) to examine the historical development of CF research based on 1,528 journal articles retrieved from the Web of Science Core Collection database.

The results of the analysis reveal that CF research focuses on seven themes: sustainability, entrepreneurial finance, entrepreneurship, fintech, social entrepreneurship, social capital, and microcredits. The analysis of the four main paths reveals that equity CF has been the dominant topic in the past years. Recently, CF research has tended to focus on topics such as fintech, the COVID-19 pandemic, competition, Brexit, and policy response.

To the authors' best knowledge, this is the first attempt to explore knowledge diffusion dynamics in the CF field. Overall, the study offers a structure for analyzing the paths through which knowledge is diffused, enabling scholars to effectively manage a large volume of research papers and gain a deeper understanding of the historical, current, and future trends in the development of CF.

Measuring research’s policy influence is challenging, given the complexity of the policy process, the gradual nature of policy influence, and the time lag between research investment and impact. This paper assesses measurement approaches and discusses their merits and applications to overcome various hurdles.

Relevant articles and studies were selected and analyzed. First, the research-policy interface was revisited to understand their link and how research influences policy making. Second, the most common approaches for measuring policy influence were reviewed based on their features, strengths, and limitations.

The three approaches reviewed — pyramid, influencing, and results chain — have their respective strengths. Thus, research organizations planning to design a program for monitoring and evaluation (M&E) of policy influence have to adopt the best possible features of each approach and develop a customized method depending on their objectives and overall M&E framework.

This paper fosters a deeper understanding of leveraging the three approaches.

The growth in the number of nongovernmental organizations (NGOs) worldwide has led to increased competition for donations. A stronger NGO brand equity will make donors more attracted to an organization, compelling them to increase both their donations and their commitment. The goal of this study is to propose a novel donor-based brand equity model. The present study takes into consideration the special characteristics that donors confer to NGOs—specific examples of nonprofit organizations (NPOs) that demand higher moral capital. The suggested framework considers the donor's perspective of NGO brand equity and identifies new dimensions: familiarity (recall, brand strength and brand identification), associations (authenticity, reputation and differentiation) and commitment (attitudinal, emotional) by building on previous NPOs and consumer-based brand equity models.

Based on the analysis of the literature, the authors propose an NGO donor-based brand equity model, which the authors test with a convenience sample of 137 individuals through partial least squares structural equation modeling.

The results of this study demonstrate the positive effects of brand reputation, brand differentiation, brand identification and brand commitment on donor-based brand equity.

The novel proposed model will help NGO managers better understand the sources of brand equity from the donor's perspective and more efficiently manage their resources and activities to strengthen their NGO's brand equity.

This paper provides a novel, multidimensional NGO donor-based brand equity model that is oriented to the specific characteristics of NGOs; this orientation distinguishes it from previous NPOs and commercial brand equity models.

隨著全球的非政府組織的數目不斷增加, 爭取捐款的競爭也日趨激烈。一個強大的非政府組織品牌資產, 會吸引捐款者、使其對該組織更為關注, 因而驅使他們增加捐助和支持。本研究擬提出一個新穎的、以捐助者為基礎的品牌資產模型。我們這個建議, 考慮了捐助者賦予非政府組織的一些特徵, 而這些非政府組織是一些強烈要求更高道德資本的特殊例子。我們建議的框架, 考慮了捐助者如何從其角度看待非政府組織的品牌資產, 亦建立了新的層面, 這包括熟悉度 (回憶、品牌強度、品牌識別) 、關聯 (真確性、聲譽、差異化) 、以及支持度 (在態度上的、或在情感上的); 建立這框架, 是透過研究以往的非政府組織、以及以消費者為基礎的品牌資產模型, 並以此為基礎而完成的。

我們分析有關的文獻, 並以此為基礎, 提出一個以捐助者為基礎的非政府組織的品牌資產模型。我們使用偏最小平方法的結構方程模型, 並測試了隨便抽樣的137個獨立個體, 來試驗這個新模型。

研究結果顯示, 品牌信譽、品牌差異化、品牌識別和品牌忠誠度, 均會對以捐助者為基礎的品牌資產帶來正面的影響。

我們提出的新模型, 讓非政府組織的管理人員能從捐助者的角度、去進一步瞭解品牌資產的來源, 從而更能有效地管理資源和組織的活動, 以便強化組織的品牌資產。

研究提供了以非政府組織特徵為導向的一個、以捐助者為基礎的新穎而俱多層面的非政府組織品牌資產模型。這個研究取向、有別於過往的非營利組織或商業性的品牌資產模型。

This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.

This study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.

The study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.

This study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.

Using a large-scale school improvement program in Sweden as a case, this article aims to explore the state governance of a large-scale school improvement program in Sweden and how officials at the state agency level made sense of the reform ideas and operationalized them in policy actions.

Data were integrated from Swedish Government Official Reports and formal directives from the Ministry of Education. Officials of the Swedish National Agency for Education (SNAE) were also interviewed. Data were analyzed to identify how regulatory rules, professional norms and cultural–cognitive beliefs shaped SNAE's design of the program.

The article shows how different types of governance (i.e. regulatory rules, professional norms and cultural–cognitive beliefs) set the direction for managing large-scale school improvement. In particular, in the studied case, the lack of clear regulatory directives enabled sensemaking processes clearly influenced by normative ideas and cultural–cognitive beliefs.

The findings are mostly presented from the perspective of managers, so further study is required to attain a broader understanding of the state agency level's role and function.

By illustrating the strengths of understanding various dimensions of educational governance, the findings are highly relevant to both policymakers and educational managers at different levels of school systems.

The article offers a valuable perspective on large-scale school improvement and educational governance by focusing on a level that has hitherto received little attention.