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Research related to workplace accommodation requests for employees with mental illness is scarce, though evidence suggests that these individuals often fail to request accommodations even when needed. The authors' research study aimed to address these shortcomings by (1) assessing employees' knowledge of Americans with Disabilities Act (ADA) laws and how this knowledge influences employees' perceived need for and requests of accommodations; (2) examining the relationship between employees' perceived need for accommodations and employees' workplace outcomes and (3) examining the relationship between perceived need for accommodations and employees' actual accommodation requests, as well as how stigma influences this relationship.

The authors used two survey studies to investigate their research questions. Study 1 participants were recruited through Amazon's MTurk, and Study 2 participants were recruited through support groups for individuals diagnosed with mood disorders (i.e. depression and bipolar disorder).

The authors found significant gaps in both subjective and objective ADA-related knowledge among participants in their sample. The authors' Study 1 results also revealed an interaction between the perceived need for accommodations and accommodation requests in predicting job satisfaction and turnover intentions. When employees needed accommodations but did not request them, it resulted in worsened workplace outcomes. In Study 2, the authors aimed to identify barriers to requesting accommodations. The authors found that the relationship between perceived need for accommodations and actual accommodation requests was moderated by both public and self-stigma, thereby showing that stigma can impede individuals from requesting needed accommodations at work.

The authors' study sheds light on a population that has been relatively understudied in the workplace accommodations literature, namely those with mental illness. The authors first identify the perceived need for accommodations as an important factor in making accommodations requests at work, as prior work has failed to differentiate how the need for accommodations can vary across individuals. Next, the authors show how workplace outcomes (i.e. job satisfaction and turnover intentions) are negatively affected when employees need accommodations but do not request them. Finally, the authors demonstrate how both public stigma and self-stigma can reduce the likelihood that individuals request accommodations at work, even when needed.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

This study investigates the diversification benefits of multiple cryptocurrencies and their usefulness as investment assets, individually or combined, in enhancing the performance of a well-diversified portfolio of traditional assets before and during the pandemic COVID-19.

This paper uses two optimization techniques, namely the mean-variance and the maximum Sharpe ratio. The naïve diversification rules are used for comparison. Besides, the Sharpe and the Sortino ratios are used as performance measures.

The results show that cryptocurrencies diversification benefits occur more during the COVID-19 pandemic rather than before it, with the maximum Sharpe ratio portfolio presenting its highest performance. Furthermore, the results suggest that, during COVID-19, the diversification benefits are slightly better when using a combination of cryptocurrencies to an already well-diversified portfolio of traditional assets rather than individual ones. This serves to improve the performance of the maximum Sharpe ratio portfolio, and to some extent, the naïve portfolio. Yet, cryptocurrencies, whether added individually or combined to a well-diversified portfolio of traditional assets, don't fit in the minimum variance portfolio. Besides, the efficient frontier during COVID-19 pandemic dominates the one before COVID-19 pandemic, giving the investor a better risk-return trade-off.

To the best of the author's knowledge, this is the first study that examines the diversification benefits of multiple cryptocurrencies both as individual investments and as additional asset classes, before and during COVID-19 pandemic. The paper covers all analyses performed separately in previous studies, which brings new evidence regarding the potential for cryptocurrencies in portfolio diversification under different portfolio strategies.

Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).

This review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.

The result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.

Basically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.

Therefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.

The social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.

Remote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.

The main aim of this study is to understand the role of smart technologies and show the rankings of various smart technologies in collection and classification of electronic waste (e-waste).

This study presents a framework integrating the concepts of collection and classification mechanisms and smart technologies. The criteria set includes three main, which are economic, social and environmental criteria, including a total of 15 subcriteria. Smart technologies identified in this study were robotics, multiagent systems, autonomous tools, smart vehicles, data-driven technologies, Internet of things (IOT), cloud computing and big data analytics. The weights of all criteria were found using fuzzy analytic network process (ANP), and the scores of smart technologies which were useful for collection and classification of e-waste were calculated using fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR).

The most important criterion was found as collection cost, followed by pollution prevention and control, storage/holding cost and greenhouse gas emissions in collection and classification of e-waste. Autonomous tools were found as the best smart technology for collection and classification of e-waste, followed by robotics and smart vehicles.

The originality of the study is to propose a framework, which integrates the collection and classification of e-waste and smart technologies.

The purpose of this study is to discuss the most relevant literature related to the adoption of International Public Sector Accounting Standards (IPSAS) in the public sector in developed and developing countries, identifying the constraints and stimuli they represent in the implementation of the public accounting reform. It also presents future research proposals on the factors identified.

The methodology is based on a systematic review of the literature described by Moher et al. (2009). The final sample includes 90 academic papers published from 2000 to 2022.

The main findings indicate that there are differences between constraints and stimuli in the implementation of accounting standards between developed and developing countries. In terms of constraints, the main factor in developed countries is the lack of training, whereas in developing countries it is the limitation on financial resources. In addition, the results demonstrate that in developed countries the factors that most encourage the implementation of accounting standards are modernization and improvement of accounting, while in developing countries, encouragement comes mainly from external and internal pressure.

This study helps countries and institutions to learn from experience and better prepare for the accounting reforms of public administration that they will undertake. Managers of public organizations may be willing to make decisions in the adoption of IPSAS if they take into account the factors established herein.

This study helps countries and institutions to learn from the experience, better prepare for the public administration accounting reforms that they will undertake and add greater transparency in the accountability of public accounts to citizens.

In addition to previous studies, this study addresses a number of factors perceived by those involved in the implementation of IPSAS in developed and developing countries and provides a robust research agenda to pursue during the coming years, as there are several important unexplored questions that invite further research.

This study aims to investigate the impact of earnings predictability and truthfulness on nonprofessional investors’ investment willingness.

Earnings predictability is captured by quarterly earnings autocorrelation, and earnings truthfulness is indicated by real earnings management (REM). The average of investment attractiveness and willingness measures investment willingness. The authors use experiments to isolate the impact of quarterly earnings autocorrelation and REM on investors’ investment behaviors.

From the 2 × 2 design, the authors observe that investors weight more on earnings predictability than earnings truthfulness.

The generalization of the findings may be constrained for the following reasons. First, the authors use only one proxy, REM, to measure earnings truthfulness. In addition, the authors provide the participants, Amazon Mechanical Turk, with earnings predictability. Results may no longer hold if each participant has different understanding and analysis of earnings predictability.

In periods of unprecedented and severe financial uncertainty (i.e. the COVID-19 pandemic), investors rely more on earnings predictability than on earnings truthfulness. The study assists managers to strategically emphasize the predictability of earnings to attract investors, especially when firms face financial challenges or uncertainty.

This study contributes to understanding investor behavior and the critical role of earnings predictability and truthfulness in shaping investment decisions.

This paper contributes to the literature of earnings properties in financial reporting, particularly by shedding light on the nuanced interplay between earnings predictability and earnings truthfulness. The research also demonstrates that elevated earnings autocorrelation indirectly stimulates investment willingness by enhancing the investors’ perception of earnings persistence of targeted firms.

It has been suggested that homicides in Jamaica are partly driven by conflicts among criminals over funds coming from international lottery scams; most of these funds are channeled into the country via remittances. This study aims to determine the empirical relationship between remittances and homicides in Jamaica over the period 1985–2019.

The authors apply an error correction modelling framework while accounting for indicators of changes in socioeconomic conditions.

There are two. First, the authors find from impulse response analysis of the long-run dynamics that an increase in remittances is associated with an increase in homicides, and vice versa. Second, the authors find that there is bidirectional Granger causality between remittances and homicides in the short run.

Two important implications are that policies should be strengthened to channel remittances to productive and legal investment opportunities and that greater efforts may be needed to stem the flow of funds coming from international lottery scamming and other illegal activities.

This is the first study that examines the dynamic relationship between remittances and homicides in Jamaica from a robust statistical perspective.

While some countries have used debt to drive economic growth, the asymmetric effect on sub-Saharan African (SSA) countries has received little attention in the empirical literature. This paper therefore examines the asymmetric effect of external debts on economic growth.

The panel nonlinear autoregressive distributed lag (NARDL) approach was employed in the study for 29 sub-Saharan African countries from 1990 to 2021. The cross-sectional dependence test was used to determine the presence of cross-sectional dependence, while the second-generation panel unit root tests was used to examine the unit-root properties.

The empirical results show that external debt has an asymmetric effect on economic growth in both the short and long run. In the long run, a positive shock in external debts of 1% triggers an upturn in economic growth by 0.216% while a negative shock triggers 0.354% decline in economic growth. This implies that the negative shock of external debts has a much stronger impact on economic growth than the positive shock. In the short run, a positive shock in external debts by 1% triggers a decline in economic growth by 0.641%, while a negative shock of 1% triggers a fall in economic growth of 0.170%.

The paper used the NARDL model to examine the asymmetric impact of external debt on the economic growth of SSA countries, which has not been extensively studied. It is recommended that governments in the selected countries in sub-Saharan Africa should drive economic growth by promoting domestic revenue mobilization since external debts impede economic growth.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.