Search results

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

In a world with over 8 billion people, ensuring sustainable food sources is paramount. This chapter explores the pivotal role of aquaculture in addressing the challenges of marine conservation and sustainable resource use. Aligned with the United Nations’ Sustainable Development Goal 14, aquaculture emerges as a solution to relieve pressure on wild fish stocks and enhance food security. The chapter emphasises the rapid growth of this sector and underscores the importance of international cooperation and policies like the Global Ocean Treaty in ensuring marine biodiversity. While acknowledging the potential of aquaculture, the chapter delves into environmental concerns surrounding fishmeal and fish oil in feed. It advocates for innovative technologies and ingredients to establish a circular bioeconomy. The significance of higher education in advancing sustainable aquafeed technology, breeding, and genetics is highlighted, with a discussion on milestones achieved by experts like Dr John E. Halver and Professor Simon J. Davies. Examining technological advances, the chapter explores molecular genetics, transgenics, and gene editing, particularly CRISPR biosciences, as transformative tools for enhancing aquaculture productivity and sustainability. Environmental impacts are addressed, proposing solutions such as Recirculating Aquaculture Systems (RAS) and Multitrophic Aquaculture Systems (MTA) to minimise ecological footprints. Throughout, there is a strong emphasis on the integral role of research and education in fostering sustainable aquaculture practices. The chapter advocates for specialised courses and programs in higher education to prepare the next generation for the challenges and opportunities in aquaculture, ensuring its contribution to global food security and environmental stewardship.

Objectively evaluating the fairness of the algorithm, exploring in specific scenarios combined with scenario characteristics and constructing the algorithm fairness evaluation index system in specific scenarios.

This paper selects marketing scenarios, and in accordance with the idea of “theory construction-scene feature extraction-enterprise practice,” summarizes the definition and standard of fairness, combs the application link process of marketing algorithms and establishes the fairness evaluation index system of marketing equity allocation algorithms. Taking simulated marketing data as an example, the fairness performance of marketing algorithms in some feature areas is measured, and the effectiveness of the evaluation system proposed in this paper is verified.

The study reached the following conclusions: (1) Different fairness evaluation criteria have different emphases, and may produce different results. Therefore, different fairness definitions and standards should be selected in different fields according to the characteristics of the scene. (2) The fairness of the marketing equity distribution algorithm can be measured from three aspects: marketing coverage, marketing intensity and marketing frequency. Specifically, for the fairness of coverage, two standards of equal opportunity and different misjudgment rates are selected, and the standard of group fairness is selected for intensity and frequency. (3) For different characteristic fields, different degrees of fairness restrictions should be imposed, and the interpretation of their calculation results and the means of subsequent intervention should also be different according to the marketing objectives and industry characteristics.

First of all, the fairness sensitivity of different feature fields is different, but this paper does not classify the importance of feature fields. In the future, we can build a classification table of sensitive attributes according to the importance of sensitive attributes to give different evaluation and protection priorities. Second, in this paper, only one set of marketing data simulation data is selected to measure the overall algorithm fairness, after which multiple sets of marketing campaigns can be measured and compared to reflect the long-term performance of marketing algorithm fairness. Third, this paper does not continue to explore interventions and measures to improve algorithmic fairness. Different feature fields should be subject to different degrees of fairness constraints, and therefore their subsequent interventions should be different, which needs to be continued to be explored in future research.

This paper combines the specific features of marketing scenarios and selects appropriate fairness evaluation criteria to build an index system for fairness evaluation of marketing algorithms, which provides a reference for assessing and managing the fairness of marketing algorithms.

Algorithm governance and algorithmic fairness are very important issues in the era of artificial intelligence, and the construction of the algorithmic fairness evaluation index system in marketing scenarios in this paper lays a safe foundation for the application of AI algorithms and technologies in marketing scenarios, provides tools and means of algorithm governance and empowers the promotion of safe, efficient and orderly development of algorithms.

In this paper, firstly, the standards of fairness are comprehensively sorted out, and the difference between different standards and evaluation focuses is clarified, and secondly, focusing on the marketing scenario, combined with its characteristics, key fairness evaluation links are put forward, and different standards are innovatively selected to evaluate the fairness in the process of applying marketing algorithms and to build the corresponding index system, which forms the systematic fairness evaluation tool of marketing algorithms.

This study aimed to address the underexplored domain of organisational vulnerability, with a specific focus on understanding how vulnerability is understood in organisations and the underlying pathways leading to vulnerability.

This study utilised a narrative literature review methodology, using Google Scholar as the primary source, to analyse the concepts of organisational vulnerability in the context of disaster risk studies. The review focused on relevant documents published between the years 2000 and 2022.

The analysis highlights the multifaceted nature of organisational vulnerability, which arises from both inherent weaknesses within the organisation and external risks that expose it to potential hazards. The inherent weaknesses are rooted in internal vulnerability pathways such as organisational culture, managerial ignorance, human resources, and communication weaknesses that compromise the organisation’s resilience. The external dimension of vulnerability is found in cascading vulnerability pathways, e.g. critical infrastructure, supply chains, and customer relationships.

As the frequency and severity of disasters continue to increase, organisations of all sizes face heightened vulnerability to unforeseen disruptions and potential destruction. Acknowledging and comprehending organisational vulnerability is a crucial initial step towards enhancing risk management effectiveness, fostering resilience, and promoting sustainable success in an interconnected global environment and an evolving disaster landscape.

Since March 2020, the global COVID-19 pandemic has disproportionately impacted refugees by compounding preexisting and systemic health, social and economic inequities. In Canada, approximately 50,000 Syrian refugees arrived between 2015 and 2020 and were in the process of rebuilding their lives when the pandemic started. This study aims to explore the impact of the COVID-19 pandemic for Syrian refugees in Canada and identify supports needed.

Drawing on frameworks for refugee psychosocial adaptation and social integration and a qualitative descriptive design, the study used thematic analysis to examine semi-structured interviews with 10 Syrians.

Findings indicated four themes that provide a snapshot of impacts relatively early in the pandemic: facing ongoing development, inequity and insecurity during integration; disruption of settlement, integration and adaptation due to the pandemic; ongoing adaptation and resilience during integration in Canada; and ongoing needs and solutions for integration and adaptation.

This study builds upon growing research concerning Syrian refugees and psychosocial adaptation, particularly during the pandemic. The findings highlight the impacts of the pandemic on a population already facing inequities in a resettlement country. While the findings emphasize the resilience of the Syrian refugee community, the study also demonstrates the need for ongoing supports and justice-oriented action to fulfill resettlement commitments, especially in the face of additional stressors like the COVID-19 pandemic. Implications for policy, practice and future research are discussed.

Foreign subsidiaries of multinational enterprises (MNEs) operate in complex and competitive international environments, implement market and non-market strategies, manage resources and value-added activities and contribute to the overall performance of their parent firms. Thus, the research question on the determinants of MNE foreign subsidiaries’ performance is of interest to managers and academic researchers. The empirical literature has flourished over the recent decades; however, the domains are fragmented, and the findings are inclusive. The purpose of this study is to systematically review, analyse and synthesize the empirical articles in this area, identify research gaps and suggest a future research agenda.

This study uses the qualitative content analysis method in reviewing and analysing 150 articles published in 24 scholarly journals during the period 2000–2023.

The literature uses a variety of theoretical perspectives to examine the key determinants of subsidiary performance which can be grouped into six major domains, namely, home- and host country-level factors; distance between home and host countries; the characteristics of parent firms and of subsidiaries; and governance mechanisms (the establishment modes and ownership strategy, subsidiary autonomy and the use of home country expatriates for transferring knowledge from the headquarters and controlling foreign subsidiaries). A range of objective and subjective indicators are used to measure subsidiary performance. Yet, the research shows a lack of broader integration of theories and presents inconsistent theoretical predictions, inconclusive empirical findings and estimation bias, which hinder our understanding of how the determinants independently and jointly shape the performance of foreign subsidiaries.

This study provides a comprehensive, nuanced and systematic review that synthesizes and clarifies the determinants of subsidiary performance, offers deeper insights from both theoretical, methodological and empirical aspects and proposes some promising avenues for future research directions.

This study aims to delve into the relationship between open innovation and organizational effectiveness, expanding upon previous research that primarily focused on the impact of open innovation on firm performance.

Based on data collected from top- and middle-level managers across diverse sectors in India, the authors examined the intricate dynamics of open innovation and its effects on organizational effectiveness. The authors took two approaches to examine the data; using structural equation modeling and using the fuzzy set qualitative comparative analysis (fsQCA) approach.

This empirical evidence underscores the potential advantages of adopting open innovation practices within organizations. The contribution extends to both theoretical and practical domains.

Theoretically, this research enriches the literature on open innovation and organizational effectiveness by providing empirical substantiation for their interconnection.

From a practical perspective, the findings offer actionable insights for practitioners and organizational leaders, suggesting that embracing open innovation can significantly enhance organizational effectiveness, ultimately fostering improved performance and competitiveness. The findings also have implications for external stakeholders aiming to engage with innovation-driven organizations for purposes of commercialization and knowledge exchange.

This study advocates for incorporating inbound and outbound open innovation practices within strategic decision-making processes to achieve organizational effectiveness.

The wine market has witnessed intensified competition across all aspects. However, there is a limited systematic assessment of wine marketing literature. This study employs bibliometric and thematic content evaluation methodologies to present a comprehensive summary and trend of the existing research in wine marketing.

This integrates bibliometric and thematic content analytical techniques to trace the evolutionary arc of wine marketing research, underpinned by a framework-based evaluation - the 4 Ws (what, where, why, and how) to give a thorough overview of wine marketing literature. We draw on the thematic structure of wine marketing research by conducting keyword co-occurrence, thematic, social network, and cluster-based content analyses. A total of 291 publications from peer-reviewed journals, spanning the years 2001–2023, were examined in the present study.

The bibliometric analysis is used to determine the most notable journals, authors, nations, articles, and themes, thereby offering a comprehensive understanding of the publication trends within the sphere of wine marketing. Based on the findings, this study advocates a research agenda to extend further contributions to the field of wine marketing.

The study performs a comprehensive analysis of the publication patterns, influential authors, and emerging trends within the wine marketing research field. It provides a unique insight into the evolution and development of wine marketing as a scholarly discipline, facilitating future research directions and knowledge advancement.

The present study examines the fluctuations in Socioeconomic and demographic (SED) factors and the prevalence of Non-Communicable Diseases (NCDs) across clusters of states in India. Further, it attempts to analyze the extent to which the SED determinants can serve as predictive indicators for the prevalence of NCDs.

The study uses three rounds of unit-level National Sample Survey self-reported morbidity data for the analysis. A machine learning model was constructed to predict the prevalence of NCDs based on SED characteristics. In addition, probit regression was adopted to identify the relevant SED variables across the cluster of states that significantly impact disease prevalence.

Overall, the study finds that the disease prevalence can be reasonably predicted with a given set of SED characteristics. Also, it highlights age as the most important factor across a cluster of states in understanding the distribution of disease prevalence, followed by income, education, and marital status. Understanding these variations is essential for policymakers and public health officials to develop targeted strategies that address each state’s unique challenges and opportunities.

The study complements the existing literature on the interplay of SEDs with the prevalence of NCDs across diverse state-level dynamics. Its predictive analysis of NCD distribution through SED factors adds valuable depth to our understanding, making a notable contribution to the field.