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This paper aims to study the impact of foreign direct investment (FDI) and official development assistance (ODA) on the economic growth of developing countries. This paper classifies sample countries into two groups (low- and high-income developing countries) based on income level and investigates whether the two sources of foreign capital have different effects on the economic development of each subgroup of countries.

The authors analyze panel data on 93 countries from 1981 to 2020 using a two-stage least squares (2SLS) estimation. The 2SLS method is used to overcome the endogeneity problem between economic growth and FDI. The sources of the data are World Bank and OECD.

First, FDI inflows tend to accelerate per capita GDP growth in both total sample countries and within both groups of countries. Second, ODA has a significant impact on per capita GDP growth only for low-income developing countries. This result indicates that ODA seems to be particularly important for low-income developing countries.

This paper suggests policy implications that low-income developing countries should create an environment for more ODA funds to flow into themselves with efforts such as improving the credibility and effectiveness of the government related to ODA programs. It also provides implications for donors of ODA to focus their ODA resources on low-income developing countries to more effectively achieve the goal of helping developing countries’ economic growth.

This paper investigates whether FDI and ODA have different effects on the economic development of low- and high-income developing countries. To the best of authors’ knowledge, this point is not addressed in existing studies.

Food safety is among the most important topics in the world. According to WHO guidelines, aflatoxins are one of the most hazardous food toxins. Therefore, their detection in food products seems crucial due to health problems. The purpose of this paper is to discuss the different types of biosensors in aflatoxin determination.

Traditional detection methods are time consuming and expensive. As fast and accurate detection is important in monitoring food contaminants, alternative analytical methods would be essential. Biosensors are the intelligent design of sensitive sensors for precise detection of toxins in a short time. Various biosensors are being applied for aflatoxins detection in food products with many advantages over the traditional methods.

Biosensors are cost-effective, stable and have possessed high selectivity, specificity and accuracy in aflatoxins detection. Applying biosensors has been increased recently, so biosensing methods (optical, electrochemical, piezoelectrical, immunosensors, surface plasmon resonance and calorimetric) are discussed along with their advantages in this article.

More efforts should be occurred to detect and decrease the aflatoxins by biosensors, and some traits like accuracy and selectivity would be the purpose of future projects. The combination of various techniques would also help in toxin detection issue in food products, so high efforts in this regard are also required for the upcoming years.

This article also reviews different types of biosensors simultaneously and explains their specificity for aflatoxin determination in different food products and also the future trends and requirements.

This study aimed to examine the efficient market hypothesis (EMH) for the stock markets of 12 member countries of the Organization of Islamic Cooperation (OIC), such as Egypt, Indonesia, Jordan, Kuwait, Malaysia, Morocco, Pakistan, Saudi Arabia, Tunisia, Turkey and the United Arab Emirates (UAE), during the global financial crisis (GFC) and the COVID-19 (CV-19) epidemic. The objective was to classify the effects on individual indices.

The study employed the multifractal detrended fluctuation analysis (MF-DFA) on daily returns. After calculation and analysis, the data were then divided into two significant events: the GFC and the CV-19 pandemic. Additionally, the market deficiency measure (MDM) was utilized to assess and rank market efficiency.

The findings indicate that the average returns series exhibited persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. The study employed MF-DFA to analyze the sequence of normal returns. The results suggest that the average returns series displayed persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. Furthermore, all markets demonstrated efficiency during the two crisis periods, with Turkey and Tunisia exhibiting the highest and deepest levels of efficiency, respectively. The multifractal properties were influenced by long-range correlations and fat-tailed distributions, with the latter being the primary contributor. Moreover, the impact of the fat-tailed distribution on multifractality was found to be more pronounced for indices with lower market efficiency. In conclusion, this study categorizes indices with low market efficiency during both crisis periods, which subsequently affect the distribution of assets among shareholders in the stock markets of OIC member countries.

Multifractal patterns, especially the long memory property observed in stock markets, can assist investors in formulating profitable investment strategies. Additionally, this study will contribute to a better understanding of market trends during similar events should they occur in the future.

This research marks the initial effort to assess the impact of the GFC and the CV19 pandemic on the efficiency of stock markets in OIC countries. This undertaking is of paramount importance due to the potential destabilizing and harmful effects of these events on global financial markets and societal well-being. Furthermore, to the best of the authors’ knowledge, this study represents the first investigation utilizing the MFDFA method to analyze the primary stock markets of OIC countries, encompassing both the GFC and CV19 crises.

The purpose of this paper is to revisit previous design principles and guidelines for online makerspaces in public libraries (Kim et al., 2020) and expand the design principles with more updated implications and examples from the literature published during and after the COVID-19 pandemic.

The authors reviewed recently published papers about online transitions of makerspaces, especially during and after the COVID-19 pandemic, summarized their implications and deduced applicable design principles and guidelines.

This paper proposes updated design principles and guidelines based on four key areas: Program and service design; Tools and materials; Facilitation; and Logistic support. These updated design principles considered a wider range of patrons that public library makerspaces may serve, the digital divide issues and logistic concerns that should be addressed beyond the scope of a single makerspace.

This paper compiles various lessons learned and strategies regarding online makerspaces and maker programming for public libraries and provides helpful design principles and guidelines for the continued use of online components for makerspace services and programs.

Examining dominance in influencing leadership perceptions for men and women has received significant scholarly attention. The studies typically show that dominance is beneficial for men in attaining leadership positions but not for women. However, the studies were predominantly conducted more than two decades ago. Given the developments in gender research, this study extends the dominance line of inquiry by probing the impact of dominance need on leader emergence for men versus women in self-managed work teams. Furthermore, this study aims to examine if team dominance needs dispersion posits as a boundary condition for the combined impact of dominance needs and gender on leader emergence.

The authors conducted a longitudinal study that lasted one semester and involved 44 ad hoc self-managed work teams.

This study found that dominance needs facilitated leader emergence regardless of gender, and team dominance needs dispersion. Furthermore, men with high dominance needs were likelier to emerge as leaders than women with high dominance needs in high dominance needs dispersion teams. By contrast, women low in dominance needs received a harsher penalty in their leadership emergence than men low in dominance needs in low dominance needs dispersion teams

These results depart from the usual findings regarding the backlash effects that dominant women face and paint a rosy picture regarding the use of dominance in shaping leader emergence. However, the findings support the notion in gender stereotypes research that women are judged more critically than men in ascending to leadership positions.

Food waste among young consumers is a significant concern that threatens food sustainability due to consumption behaviour. This study has integrated the Theory of Interpersonal Behaviour to extend the Theory of Planned Behaviour to measure young consumer’s intention to reduce food waste in a developing country context. Bringing in emotion, habits, and facilitating conditions to predict the consumers' attitudes, this study assesses the intention to reduce food waste for corporate sustainability from the perspective of consumption.

Using a quantitative approach and purposive sampling, 205 samples were collected from the young generation aged 18 to 30, who frequently have meals at home. The PLS-SEM technique was employed to examine the hypothesised model.

The findings supported all the hypotheses mentioned where attitudes, subjective norms (SN) and perceived behavioural control (PBC) affect the intention to reduce food waste among young consumers. Furthermore, the other variables, namely, habits, emotions and facilitating conditions, also significantly impact the attitudes of the young generation.

Understanding young consumers' food waste behaviour is vital from the social, economic, and environmental perspectives. This study showcases a comprehensive food waste behaviour model among young consumers by integrating the Theory of Interpersonal Behaviour and the Theory of Planned Behaviour to examine the intention to minimise food waste. Practically, this study offers insights to business practitioners and communities in managing food waste from the young consumer perspective. Socially, this study supports the United Nations' (UN) Sustainable Development Goals (SDG) 12 by contributing to global food waste reduction efforts.

This paper aims to investigate the cooperative governance mechanisms for personal information security, which can help enrich digital governance research and provide a reference for the formulation of protection policies for personal information security.

This paper constructs an evolutionary game model consisting of regulators, digital enterprises and consumers, which is combined with the simulation method to examine the influence of different factors on personal information protection and governance.

The results reveal seven stable equilibrium strategies for personal information security within the cooperative governance game system. The non-compliant processing of personal information by digital enterprises can damage the rights and interests of consumers. However, the combination of regulatory measures implemented by supervisory authorities and the rights protection measures enacted by consumers can effectively promote the self-regulation of digital enterprises. The reputation mechanism exerts a restricting effect on the opportunistic behaviour of the participants.

The authors focus on the regulation of digital enterprises and do not consider the involvement of malicious actors such as hackers, and the authors will continue to focus on the game when assessing the governance of malicious actors in subsequent research.

This study's results enhance digital governance research and offer a reference for developing policies that protect personal information security.

This paper builds an analytical framework for cooperative governance for personal information security, which helps to understand the decision-making behaviour and motivation of different subjects and to better address issues in the governance for personal information security.

Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies.

Occupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes.

The results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes.

The proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents.

Practitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being.

The literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results.

This literature review aims to portray the thematic landscape of the Pacific Accounting Review (PAR) from 2013 to 2023. This paper also synthesises the special issues in PAR and identifies the main research streams that facilitate contemplating the dialogic interactions between PAR and real-world challenges. Furthermore, this paper aligns these streams with the emerging concerns in Sustainable Development Goals (SDGs) and technological disruptions to propose impactful future directions for publications in PAR.

This review adopts bibliometric analysis to establish the main research streams and objective measures for directing future publications. This paper acquires the data of 310 PAR articles from the Web of Science and ensure the data integrity before the analysis. Based on this technique, this paper also analyses PAR’s productivity, authorship and local and global impacts.

Our bibliometric analysis reveals three key research streams: (1) ESG practices and disclosures, (2) informal institutions in accounting and (3) accounting in transition. This finding affirms PAR’s relevance to real-world accounting challenges. Using a thematic map, this paper portrays the current state of PAR’s topics to identify potential directions for future publications. Further, this paper proposes three future paths for PAR: (1) the research agenda for non-financial reporting, (2) research relating to and from diverse countries considering both formal and informal contemporary contextual factors and (3) the future of the evolving accounting profession.

This study adds value to the existing PAR reviews by extending our knowledge with the latest publications, demonstrating an objective and replicable approach, and offering future directions for PAR publications.