Search results

The purpose of this paper is to analyze geographic heterogeneity of consumer preferences for intrinsic quality attributes of rice in South and Southeast Asia and the drivers of demand for these attributes, with a particular focus on rice fragrance and the role of gender.

Stated-preference surveys were conducted with 4,231 urban and rural consumers in 37 cities across seven countries (Bangladesh, India, Cambodia, Indonesia, the Philippines, Thailand, and Vietnam) during 2013–2014 and analyzed through a rank-ordered logistic regression with incomplete ranking choice data.

Preferences for rice attributes are found to be significantly heterogeneous among consumers in South and Southeast Asia. Urban Thai consumers tend to prioritize appearance and cooking characteristics over taste and nutritional benefits, relative to all other surveyed consumers. In contrast with South Asian consumers, Southeast Asian consumers have largely adopted Thai preferences for rice texture and fragrance, a trend that was earlier coined “Jasminization.” We find that demand for rice fragrance is mainly driven by women, educated consumers, large families, families spending a lower share of their food expenditures on rice, and consumers in Southeast Asia (particularly the Philippines and Cambodia).

Little is known about geographic heterogeneity, drivers, and the role of gender in demand for rice fragrance. This paper fills these knowledge gaps. Our findings suggest that the more women are empowered in grocery decision-making, the more demand for aromatic rice is expected to rise. These insights can assist market-driven and gender-responsive rice breeding programs in simultaneously enhancing rice farmers' livelihoods and gender equity.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

Although current research has investigated how open research data (ORD) are published, researchers' behaviour of ORD sharing on academic social networks (ASNs) remains insufficiently explored. The purpose of this study is to investigate the connections between ORDs publication and social activity to uncover data literacy gaps.

This work investigates whether the ORDs publication leads to social activity around the ORDs and their linked published articles to uncover data literacy needs. The social activity was characterised as reads and citations, over the basis of a non-invasive approach supporting this preliminary study. The eventual associations between the social activity and the researchers' profile (scientific domain, gender, region, professional position, reputation) and the quality of the ORD published were investigated to complete this picture. A random sample of ORD items extracted from ResearchGate (752 ORDs) was analysed using quantitative techniques, including descriptive statistics, logistic regression and K-means cluster analysis.

The results highlight three main phenomena: (1) Globally, there is still an underdeveloped social activity around self-archived ORDs in ResearchGate, in terms of reads and citations, regardless of the published ORDs quality; (2) disentangling the moderating effects over social activity around ORD spots traditional dynamics within the “innovative” practice of engaging with data practices; (3) a somewhat similar situation of ResearchGate as ASN to other data platforms and repositories, in terms of social activity around ORD, was detected.

Although the data were collected within a narrow period, the random data collection ensures a representative picture of researchers' practices.

As per the implications, the study sheds light on data literacy requirements to promote social activity around ORD in the context of open science as a desirable frontier of practice.

Researchers data literacy across digital systems is still little understood. Although there are many policies and technological infrastructure providing support, the researchers do not make an in-depth use of them.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2021-0255.

The purpose of this paper is to investigate the effects of cross-country characteristics on acquirers' target status choice in cross-border mergers and acquisitions across 41 emerging markets.

The paper first reviews the existing literature and develops the related hypotheses, in conjunction with the objectives of this paper. We then describe the data employed, variable measurement and examine the effects of cross-country characteristics on the acquirers' target status choice in cross-border mergers and acquisitions while controlling for firm-level and deal-specific characteristics. The paper continues to conduct the robustness check on cross-country determinants of target status choices using the difference independent variables rather than target country-level variables only.

This research found that the likelihood of a public firm acquired relative to private one is higher if the target firm is located in countries with stronger government quality, weaker economic freedom, better financial market development and lower cultural distance between the host and home countries. The results suggest that bidders actively assess cross-country characteristics as part of their acquisition planning.

Rather than commonly analysed determinants in the previous research such as firm- and deal-specific attributes, value creation and shareholder protection, this paper indicates that institutional environments and economic conditions are closely associated with acquisition risks and benefits and have direct influences on bidder firms' acquisition bidding planning and target choice decision-making.

This paper aims to examine prospective graduate students' attitudes toward educational loan borrowing in an experimental setting.

Participants were randomly assigned to two treatment groups and one control group. Subjects in experimental group 1 received financial education: a short online course on the economic viability of getting a master's degree and how to finance it with a graduate student loan, while subjects in experimental group 2 received financial education along with information on the availability bias.

Relying on a control group in the assessment of financial literacy education intervention impacts, this research finds positive causal treatment effects on individuals’ attitudes toward debt-financed graduate education. In comparison to the control group, experimental subjects perceived the possibility of going into debt with a graduate loan to complete a master’s degree as less stressful and worrying.

This study has important educational policy implications to prevent students from stopping investing in human capital by perceiving educational loan debt as something stressful or worrying. The results can help potential (and current) grad students develop a feasible financial plan for graduate school by encouraging higher education institutions to implement educational loan information and financial education into university seminar courses for better graduate student loan decision-making.

Student attitudes toward debt have been analyzed in the context of higher education, but only a few researchers internationally have used an experimental design to study personal financial decision-making.