Search results

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

The purpose of this study is to empirically examine the impact of cosmopolitan consumer orientation (CCO) on sustainable apparel consumer behavior.

A total of 469 US responses collected using MTurk were retained for the analysis after screening for unengaged responses. Structural equation modeling was used to confirm the factor structure of the measurement model and to analyze the structural model. A two-step cluster analysis using log-likelihood distance measure and Akaike's Information Criterion was conducted to explore consumer profiles and past behavior.

Based on the model results, CCO positively impacts apparel sustainability knowledge, attitude toward purchasing sustainable apparel, perceived norm and sustainable apparel purchase intention. Attitude and perceived norm also impact sustainable purchase intention. The two-step cluster analysis, based mainly on sustainable past behavior, reveals that the group of sustainability engaged consumers knows more about apparel sustainability, has a stronger intention to purchase sustainable apparel, is more cosmopolitan and shows a higher tendency to follow social norms. Consumers in this group also tend to live in metropolitan areas and are slightly younger than unengaged consumers.

This study expands CCO research linking two major trends in society and industry: cosmopolitanism and sustainable apparel consumer behavior. The study reveals that CCO uplifts consumers' sustainable behavior and provides evidence in support of CCO as a driver of sustainable consumer behavior. Moreover, results imply a positive future outlook for the diffusion of sustainable apparel, as well as a much-needed mainstream consumer adhesion to more sustainable lifestyles. Given the repercussions of the findings, this research has numerous theoretical as well practical implications.

Implementing the 17 Sustainable Development Goals (SDGs) unarguably demands huge financial investments. However, the United Nations has acknowledged the huge financial gap militating against the implementation of the SDGs worldwide, leading experts to question the possibility of complete implementation of the goals by their terminal dateline of 2030. While the bulk of the finance currently outlaid on the SDGs comes from traditional sources including foreign direct investments (FDIs), there is the need to focus more attention on developing and exploiting impact investments that are more suitable for financing development programmes and projects. In this chapter, the SDG implementation profiles of the 12 Arab West Asia countries concerning the five most targeted SDGs were evaluated and sustainable finance issues were discussed. Secondary data were retrieved from World Bank's DataBank. The data were descriptively analyzed. Based on the profiles generated, debt relief is put forward as a possible impact investment mechanism suitable for funding the SDGs. Specifically, this chapter recommends that outright cancellation of debts based on the debt-for-SGD swap could serve as some of the impact investments needed to boost the global drive for a developed, peaceful, and just world.

To maintain the bottom line of food import risk in China, this paper proposes a novel risk state evaluation model based on bottom-line thinking after analyzing the decision-making ideas embedded in the bottom-line thinking method.

First, the order relation analysis method (G1 method) and Laplacian score (LS) are applied to calculate the constant weights of indexes. Then, the worst-case scenario of food import risk can be estimated to strive for the best result, so the penalty state variable weight function is introduced to obtain variable weights of indexes. Finally, the study measures the risk state of China's food import from the overall situation using the set pair analysis (SPA) method and identifies the key factors affecting food import risk.

The risk states of food supply in eight countries are in the state of average potential and partial back potential as a whole. The results indicate that China's food import risks are at medium and upper-medium risk levels in most years, fluctuating slightly from 2010 to 2020. In addition, some factors are diagnosed as the primary control objects for holding the bottom line of food import risk in China, including food output level, food export capacity, bilateral relationship and political risk.

This paper proposes a novel risk state evaluation model following bottom-line thinking for food import risk in China. Besides, SPA is first applied to the risk evaluation of food import, expanding the application field of the SPA method.

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.

This study aims to investigate how preservice teachers’ stages of concern, beliefs, confidence and interest in AI literacy education evolve as they deepen their understanding of AI concepts and AI literacy education.

AI literacy lessons were integrated into a technology integration course for preservice teachers, and the impacts of the lessons were evaluated through a mixed-methods study. The Concerns-Based Adoption Model was employed as the analytical framework to explore participants’ specific concerns related to AI.

Findings revealed that participants initially lacked AI knowledge and awareness. However, targeted AI literacy education enhanced preservice teachers’ awareness and confidence in teaching AI. While acknowledging AI’s educational benefits, participants expressed ongoing concerns after AI literacy lessons, such as fears of teacher displacement and the potential adverse effects of incorporating generative AI on students’ critical learning skills development.

Despite the importance of providing preservice teachers with AI literacy skills and knowledge, research in this domain remains scarce. This study fills this gap by enhancing the AI-related knowledge and skills of future educators, while also identifying their specific concerns regarding the integration of AI into their future classrooms. The findings of this study offer valuable insights and guidelines for teacher educators to incorporate AI literacy education into teacher training programs.

Knowledge transfer which refers to the communication of knowledge from a source so that it is learned and applied by a recipient has long been a challenge for knowledge management. The purpose of this study is to understand influencing factors of transactive memory system (TMS) and knowledge transfer.

Drawing on the theories of communication visibility, social distance and flow, this study develops a research model. Then, data are collected from users of the social media mobile App. Partial least squares-structural equation modeling (PLS-SEM) is employed to analyze data.

TMS is a valid second-order construct in the social media mobile app context, which is more reflected by credibility. Meanwhile, communication visibility and social distance each have positive effects on TMS which further has a positive effect on knowledge transfer. Flow has a positive effect on knowledge transfer.

Developers of the mobile App should carefully consider the role of information and communication technology (ICT) in supporting TMS and knowledge transfer. They should consider recommendation algorithm so that the benefit of communication visibility can be retained. They should design the feature to classify users based on similarity so as to stimulate users' feeling of close social distance. They should keep on improving features based on users' holistic experience.

This study incorporates the perspectives of communication visibility, social distance and flow to understand TMS and knowledge transfer, presenting a new lens for research.

The recent Russia–Ukraine conflict highlights the geopolitical importance of natural gas, especially in Europe. In this light, this study examines the impact of the Russia–Ukraine conflict on the spread of price bubbles from European natural gas to international energy prices.

The Generalized Supremum Augmented Dickey-Fuller (GSADF) test is employed to detect the occurrence of price bubble episodes while the Dynamic Logit Model is used to examine price bubble contagion between the two markets. Further, a tri-variate VAR model is used to examine the determinants of the price bubble.

The findings reveal multiple bubble episodes in both European natural gas and international energy prices. Further, evidence of bilateral contagion between European natural gas and the international energy market is found. In addition, the Russia–Ukraine conflict triggers price bubble episodes in both markets. Finally, a counterfactual analysis suggests that the conflict increases the bubble contagion from the European natural gas market to the international energy market by about 40%. These findings imply that the Russia–Ukraine conflict is a significant driver of high upside risks to bubble occurrence and subsequent contagion to both European natural gas and international energy prices.

To the best of our knowledge, this study contributes new empirical evidence that the Russian–Ukrainian conflict significantly impacts the spread of price bubbles from the European natural gas market to international energy markets.

Recently, powerful instruments for biomedical engineering research studies, including disease modeling, drug designing and nano-drug delivering, have been extremely investigated by researchers. Particularly, investigation in various microfluidics techniques and novel biomedical approaches for microfluidic-based substrate have progressed in recent years, and therefore, various cell culture platforms have been manufactured for these types of approaches. These microinstruments, known as tissue chip platforms, mimic in vivo living tissue and exhibit more physiologically similar vitro models of human tissues. Using lab-on-a-chip technologies in vitro cell culturing quickly caused in optimized systems of tissues compared to static culture. These chipsets prepare cell culture media to mimic physiological reactions and behaviors.

The authors used the application of lab chip instruments as a versatile tool for point of health-care (PHC) applications, and the authors applied a current progress in various platforms toward biochip DNA sensors as an alternative to the general bio electrochemical sensors. Basically, optical sensing is related to the intercalation between glass surfaces containing biomolecules with fluorescence and, subsequently, its reflected light that arises from the characteristics of the chemical agents. Recently, various techniques using optical fiber have progressed significantly, and researchers apply highlighted remarks and future perspectives of these kinds of platforms for PHC applications.

The authors assembled several microfluidic chips through cell culture and immune-fluorescent, as well as using microscopy measurement and image analysis for RNA sequencing. By this work, several chip assemblies were fabricated, and the application of the fluidic routing mechanism enables us to provide chip-to-chip communication with a variety of tissue-on-a-chip. By lab-on-a-chip techniques, the authors exhibited that coating the cell membrane via poly-dopamine and collagen was the best cell membrane coating due to the monolayer growth and differentiation of the cell types during the differentiation period. The authors found the artificial membrane, through coating with Collagen-A, has improved the growth of mouse podocytes cells-5 compared with the fibronectin-coated membrane.

The authors could distinguish the differences across the patient cohort when they used a collagen-coated microfluidic chip. For instance, von Willebrand factor, a blood glycoprotein that promotes hemostasis, can be identified and measured through these type-coated microfluidic chips.

This study aims to examine the impact of the COVID-19 pandemic on knowledge-sharing drivers in small- and medium-sized family firms within the restaurant and fast-food industry. The pandemic has led to significant changes in business culture and consumer behaviour, accelerating digital transformation, disruptions in global supply chains and emerging new business opportunities. These changes have also influenced knowledge sharing (KS) and its underlying drivers.

To address the research objectives, a two-phase study was conducted. In the first phase, an exploratory analysis using the Delphi method was used to identify the essential drivers and factors of KS in family businesses (FBs). This phase aimed to establish a conceptual model for the study. In the second phase, confirmatory factor analysis was conducted to analyse the impact of the COVID-19 pandemic on the identified knowledge-sharing drivers. The study examined both the pre-pandemic and post-pandemic periods to capture the shifts in attitudes towards KS.

The findings indicate a significant shift in attitudes towards knowledge-sharing drivers. Before the pandemic, organisational drivers played a central role in KS. However, after the emergence of the pandemic, technological drivers became more prominent. This shift highlights the impact of the COVID-19 pandemic on KS within FB.

The research contributes to understanding knowledge-sharing in the context of FBs and sheds light on the specific effects of the COVID-19 pandemic on knowledge-sharing drivers. The insights gained from this study can inform strategies and practices aimed at enhancing KS in similar organisational settings.