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This bibliometric analysis underscores the increasing importance of corporate sustainability in the post-COVID-19 era. Despite existing confusion and a dearth of studies on measuring corporate sustainability, the study identifies a significant methodological gap and endeavors to address it by proposing a comprehensive measure. The primary goal is to bridge this gap by conducting a bibliometric analysis on the scale of corporate sustainability, examining 126 documents spanning from 2001 to 2022. The study employs an expert opinion survey to identify and finalize dimensions and sub-dimensions of corporate sustainability, followed by a literature mapping process to formulate questionnaire items. A pilot survey is then conducted to ensure the reliability of the questionnaire. The study proposes utilizing the Organisation for Economic Co-operation and Development (OECD) index construction methodology to establish the Corporate Sustainability Index (CSI). The key findings reveal that corporate sustainability comprises economic, environmental, and social sustainability. Environmental sustainability encompasses aspects such as air, water, land, biodiversity, ocean preservation, waste prevention, and environmental management. Social sustainability involves the satisfaction of various stakeholders, including employees, shareholders, customers, community, government, nongovernmental organizations (NGOs), and suppliers. Economic sustainability is characterized by long-term profits, cost efficiency, trade-offs, sustainable investments, and spin-offs. Rooted in stakeholder theory, the proposed scale holds theoretical significance for researchers and is pertinent to policymakers striving to achieve sustainable development goals (SDGs) by 2030. Additionally, it serves as a crucial tool for practitioners and companies to assess their level of corporate sustainability.

For additive manufacturing (AM) through laser-based powder bed fusion of polymers (PBF-LB/P), accurate characterization of powder flowability is vital for achieving high-quality parts. However, accurately characterizing feedstock flowability presents challenges because of a lack of consensus on which tests to perform and the diverse forces and mechanisms involved. This study aims to undertake a thorough investigation into the flowability of eight feedstock materials for PBF-LB/P at different temperatures using various techniques.

For ambient temperature assessments, established metrics such as avalanche angle and Hausner ratio, along with the approximated flow function coefficient (FFC_app), are used. The study then focuses on the influence of elevated temperatures representative of in-process conditions. FFC_app and differential scanning calorimetry (DSC) are performed and analyzed, followed by a correlation analysis as a holistic approach to identify key aspects for flowability. Furthermore, two feedstock materials are compared with a previous study to connect the present findings to PBF-LB/P processing.

The study revealed intrinsic material properties such as mechanical softening near the melting point to become significant. This partially explains why certain powders with poor ambient temperature flowability are consistently demonstrated to produce high-quality parts. FFC_app and thermal characterization through DSC are identified as critical metrics for optimizing feedstock material characteristics across temperature ranges.

Previous studies emphasized specific characterizations of feedstock material at ambient temperature, presented a limited materials selection or focused on metrics such as shape factors. In contrast, this study addresses a partially understood aspect by examining the critical role of temperature in governing feedstock material flowability. It advocates for the inclusion of temperature variables in flowability analyses to closely resemble the PBF-LB/P process, which can be applied to material design, selection and process optimization.

This paper aims to provide a secure and efficient pairing protocol for two devices. Due to the large amount of data involving sensitive information transmitted in Internet of Things (IoT) devices, generating a secure shared key between smart devices for secure data sharing becomes essential. However, existing smart devices pairing schemes require longer pairing time and are difficult to resist attacks caused by context, as the secure channel is established based on restricted entropy from physical context.

This paper proposes a fuzzy smart IoT device pairing protocol via speak to microphone, FS2M. In FS2M, the device pairing is realized from the speaking audio of humans in the environment around the devices, which is easily implemented in the vast majority of Internet products. Specifically, to protect the privacy of secret keys and improve efficiency, this paper presents a single-round pairing protocol by adopting a recently published asymmetric fuzzy encapsulation mechanism (AFEM), which allows devices with similar environmental fingerprints to successfully negotiate the shared key. To instantiate AFEM, this paper presents a construction algorithm, the AFEM-ECC, based on elliptic curve cryptography.

This paper analyzes the security of the FS2M and its pairing efficiency with extensive experiments. The results show that the proposed protocol can achieve a secure device pairing between two IoT devices with high efficiency.

In FS2M, a novel cryptographic primitive (i.e., AFEM-ECC) are designed for IoT device pairing by using a new context-environment (i.e., human voice) . The experimental results show that FS2M has a good performance in both communication cost (i.e., 130 KB) and running time (i.e., 10 S).

Among the Somalis living in the diaspora, there is a growing number that are returning or are interested in returning to Somalia for personal, economic and political reasons. The purpose of this paper is to examine the potential impact the diaspora has on the future of Somalia by understanding the anticipatory assumptions held by young Somali-Canadians. This research will also examine the role that Soomaalinimo [1] (Somali identity) plays in the transnational ties that diasporic individuals keep with their country and its people.

Using an indigenist research approach, this paper explores the evolution of Soomaalinimo over time in a way that is culturally informed and decolonial. Young Somali-Canadians in two cities (Toronto and Edmonton) were given an opportunity to define Soomaalinimo for themselves and create scenarios of how it might evolve in the future for their great-grandchildren. An analysis of these scenarios reveals anticipatory assumptions that shape how they think about the future.

Three distinct futures scenarios emerged, and this research revealed three key anticipatory assumptions held by the participants: Somalia will always be home; returning to Somalia is important to maintain Soomaalinimo; and it is the responsibility of the previous generations to transmit Soomaalinimo to future generations. These anticipatory assumptions are examined and an analysis of the implications on decolonizing futures is presented.

This study expands the conceptualization of the future of a country to include the diaspora and uses the concept of anticipatory assumptions to reveal some of the potential implications of this group.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

The aim of this study is to offer valuable insights to businesses and facilitate better understanding on transformer-based models (TBMs), which are among the widely employed generative artificial intelligence (GAI) models, garnering substantial attention due to their ability to process and generate complex data.

Existing studies on TBMs tend to be limited in scope, either focusing on specific fields or being highly technical. To bridge this gap, this study conducts robust bibliometric analysis to explore the trends across journals, authors, affiliations, countries and research trajectories using science mapping techniques – co-citation, co-words and strategic diagram analysis.

Identified research gaps encompass the evolution of new closed and open-source TBMs; limited exploration across industries like education and disciplines like marketing; a lack of in-depth exploration on TBMs' adoption in the health sector; scarcity of research on TBMs' ethical considerations and potential TBMs' performance research in diverse applications, like image processing.

The study offers an updated TBMs landscape and proposes a theoretical framework for TBMs' adoption in organizations. Implications for managers and researchers along with suggested research questions to guide future investigations are provided.

In 2022 generative AI took the Internet world by storm. Free access to tools that can generate text and images that pass for human creations triggered fiery debates about the potential uses and misuses of generative AI in education. There has risen a need to check the popular utopian and dystopian narratives about AI against the diversity of hopes, concerns and future imaginaries that educators themselves associate with generative AI. The purpose of this study is to investigate the perspectives of Finnish teacher educators on the use of AI in education.

This article reports findings from a hands-on workshop in teacher training, where participants learned about how generative AI works, collaboratively explored generative AI and then reflected on its potential and challenges.

The results reveal nuanced, calm and thoughtful imaginaries rooted in deep understanding of educational policy, evaluation and the sociocultural context of education. The results cover teachers’ views on the impact of AI on learners’ agency, metacognition, self-regulation and more.

This article offers a unique exploration into the perceptions and imaginaries of educators regarding generative AI in specific (instead of “monolithic AI”), moving beyond dystopian views and instead focusing on the potential of AI to align with existing pedagogical practices. The educators contrasted the common techno-deterministic narratives and perceived AI as an avenue to support formative assessment practices and development of metacognition, self-regulation, responsibility and well-being. The novel insights also include the need for AI education that critically incorporates social and ethical viewpoints and fosters visions for a future with culturally, socially and environmentally sustainable AI.

This paper reports on a workshop discussing the views of the augmentative and alternative communication (AAC) community on the opportunities and risks posed by the integration of artificial intelligence (AI) into voice output communication aid systems. The views of the community on whether a Code of Practice was needed for the use of this new technology were also sought.

This was an explorative, qualitative study in which members of the AAC community attending a session at a UK national conference were invited to discuss the topic, responding to structured questions from the research team. The use of AI for both novel language generation and rate enhancement was discussed within the session.

Many potential opportunities and benefits of AI to AAC users were discussed by the group. Risks associated with new and existing biases in AI language models were raised, as was the need to ensure that outputs generated by AI were authentically authored by users. Whilst there was broad support for the idea of a Code of Practice, questions were posed about how it would be designed and what it should contain.

This study presents a unique insight into the views of the AAC community on the benefits and risks of incorporating AI into AAC systems. The views of the community on the need for a Code of Practice may support how the field moves forward with this complex technology.

The study aims to investigate the influence of HIX.AI, an artificial intelligence (AI) tool that humanizes the generated content, on the detection capabilities of AI-generated text detectors.

The study investigates the reliability of six AI-generated content detection tools by passing ten essays, five each generated using Chat Generative Pre-Trained Transformer (ChatGPT) and Bard (Gemini) before and after passing through HIX.AI, which humanizes the AI-generated content.

The study found that the selected AI-generated text detectors identified the generated content with inconsistencies. Some of the essays were falsely identified as human-written by a few detectors, indicating that the detectors are unreliable. Post-HIX.AI application found that all the essays were passed as human-written except two, which identified as AI-generated and mixed content by two separate detectors.

The findings present the evolving field of AI-generated text detectors and the tools that can bypass the detectors highlighting the difficulties in identifying the generated content in the presence of the humanization tool. Passing the generated content as human-written has serious consequences, especially in academics. Hence, the study recommends more robust detectors to distinguish human-written and AI-generated content accurately.

The study contributes to the existing literature on AI text detectors and highlights the challenges that humanization tools pose in identifying AI-generated text by AI text detectors.

This paper has a dual purpose. The first is to provide a thorough analysis of developments in international marketing in relation to the coronavirus disease 2019 (COVID-19) pandemic; the second is to capitalize on these developments to set an agenda for future research in the field of international marketing.

This paper zooms in on and reviews the 18 papers published in International Marketing Review’s (IMR) Special Issue on “Covid 19: advancing international marketing theory and guiding practice” (2023, volume 40, issue 5). It also integrates recent research at the intersection of international marketing and the COVID-19 pandemic.

The paper highlights five areas that embody significant contemporaneous changes brought about by the COVID-19 pandemic and affect international marketing practice. These include (1) shifts in consumer behavior, (2) digitalization and artificial intelligence, (3) disruptions in supply chains, (4) communication and corporate social responsibility (CSR), and (5) international dynamic marketing capabilities. In order to advance international marketing theory in relation to pandemics and other external crises, the paper establishes research directions for each of these areas.

The paper provides a novel and comprehensive categorization of fundamental shifts caused by the COVID-19 pandemic and lays out a research roadmap to advance research in the field of International Marketing (IM). Important implications for practice are also discussed.