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This paper delves into cross-border E-business, unraveling its intricate dynamics and forecasting its future trajectory.

This paper projects the prospective market size of cross-border E-business in China for the year 2023 using the GM (1,1) gray forecasting model. Furthermore, to enhance the analysis, the paper attempts to simulate and forecast the size of China’s cross-border E-business sector using the GM (1,3) gray model. This extended model considers not only the historical trends of cross-border E-business but also the growth patterns of GDP and the digital economy.

The forecast indicates a market size of 18,760 to 18,934 billion RMB in 2023, aligning with the consistent growth observed in previous years. This suggests a sustained positive trajectory for cross-border E-business.

Cross-border e-commerce critically shapes China’s global integration and traditional industry development. The research in this paper provides insights beyond statistical trends, contributing to a nuanced understanding of the pivotal role played by cross-border e-commerce in shaping China’s economic future.

Although universities have extensive research and initiatives in place that align with the United Nations’ Sustainable Development Goals (SDGs), there is still a significant gap in documenting and assessing these efforts. This paper aims to discuss how academic libraries can apply their information management skills and open-access platforms, to facilitate the discoverability and retrieval of evidence on SDGs.

Introduced by a brief literature review on the role of libraries in contributing to the SDGs in general, the authors draw on their personal experiences as metadata specialists, participating in a project aimed at linking their university’s research output to the SDGs. A case study, from the University of Pretoria’s Veterinary Science Library, is used as an example to demonstrate the benefits of resourceful metadata in organising, communicating and raising awareness about the SDGs in the field of veterinary science.

Through practical examples and recommended workflows, this paper illustrates that metadata specialists are perfectly positioned to apply their information management skills and library platforms to facilitate the discoverability and retrieval of evidence on SDGs.

Although there are increasing reports on the contributions of libraries to support the successful implementation of the SDGs, limited information exists on the role of metadata specialists, as well as those with a practical focus.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Studies showed that construction digitalisation could prevent or mitigate accidents rate on sites. Digitalisation applications may prevent or mitigate building project collapse (BPC) but with some encumbrances, especially in developing countries. There is a paucity of research on digital technologies application to prevent or mitigate BPC in Nigeria. Thus, the research aims to explore the perceived barriers that may hinder digital technologies from preventing or mitigating building collapse and recommend measures to improve technology applications during development.

The study is exploratory because of the unexplored approach. The researchers collected data from knowledgeable participants in digitalisation and building collapse in Nigeria. The research employed a phenomenology approach and analysed collected data via a thematic approach. The study achieved saturation at the 29th interviewee.

Findings show that lax construction digitalisation implementation, absence of regulatory framework, lax policy, unsafe fieldworkers' behaviours, absence of basic infrastructure, government attitude, hesitation to implement and high technology budget, especially in developing countries, are threats to curbing building collapse menace via digitalisation. The study identified technologies relevant to preventing or mitigating building collapse. Also, it proffered measures to prevent or mitigate building collapse via improved digital technology applications during development.

This research contributes to the construction digitalisation literature, especially in developing countries, and investigates the perceived barriers that may hinder digital technologies usage in preventing or mitigating building collapse in Nigeria.

The purpose of this paper is to present a framework of ideation pathways that organically extend the current stock of knowledge to generate new and useful knowledge. Although detailed, granular guidance is available in the strategy literature on all aspects of empirically testing theory, the other key aspect of theory development – theory generation – remains relatively neglected. The framework developed in this paper addresses this gap by proposing pathways for how new theory can be generated.

Grounded in two foundational principles in epistemology, the Genetic Argument and the open-endedness of knowledge, I offer a framework of distinct pathways that systematically lead to the creation of new knowledge.

Existing knowledge can be deepened (through introspection), broadened (through leverage) and rejuvenated (through innovation). These ideation pathways can unlock the vast, hidden potential of current knowledge in strategy.

The novelty and doability of the framework can potentially inspire research on a broad, community-wide basis, engaging PhD students and management faculty, improving knowledge, democratizing scholarship and deepening the societal footprint of strategy research.

Knowledge is open-ended. The more we know, the more we appreciate how much we don’t know. But the lack of clear guidance on rigorous pathways along which new knowledge that advances both theory and practice can be created from prior knowledge has stymied strategy research. The paper’s framework systematically pulls together for the first time the disparate elements of transforming past learning into new knowledge in a coherent epistemological whole.

This study assesses the factors influencing customers’ intention to adopt e-banking in the context of the technology acceptance model and the moderation role of cybercrime.

The variables in the study are measured using a five-point Likert scale with measures adopted from existing literature. The independent variables are perceived ease of use, perceived usefulness and security and privacy. These are postulated to be moderated by the perceived risk of cybercrime and to influence e-banking adoption intentions. A quantitative approach is used. Primary data are collected from a sample of 209 randomly selected bank customers. The study uses a two-step (measurement model and structural model) approach to data analysis.

The key findings in this study are that perceived risk of cybercrime strengthens the positive relationship between perceived ease of use and e-banking adoption intentions but dampens or weakens the positive relationship between perceived usefulness and customers’ e-banking adoption intentions. The study makes several recommendations to inform scholarship, policy and practice.

Unlike existing literature, the study makes a unique contribution by including perceived risk of cybercrime as a moderating variable of theoretical significance in the relationship between adoption of e-banking and its determinants.

The empirical study of factors related to digital transformation (DT) in the banking sector is still limited, even though the importance of the topic is universally evident. To bridge that gap, this paper aims to explore the role of digital leadership (DL), innovative culture (IC) and technostress inhibitors (TI) to support engagement for improved digital innovation (DI). Based on the literature, these variables are crucial aspects of digitalisation, even though there is no agreement on their conclusiveness.

This quantitative study tested a new conceptual model using survey data from five major banks in Libya. Partial least squares structural equation modelling was used to analyse the data from the 292 usable responses.

The results showed that DL and IC positively affect DI. Techno-work engagement (TE) mediated the relationship between leadership, culture and innovation. TI played a significant moderating role in leadership, culture and engagement relationships.

The research findings highlight critical issues about how leadership style and fostering organisational support in the banking sector can enhance DT. Leaders must demonstrate a commitment to long-term resource allocation to avoid possible negative effects from digital stress while pursuing DI through work engagement.

The study suggests that fostering organisational support can enhance DT in retail banks, potentially leading to improved customer experiences and increased access to financial services. These programs will help banks contribute to societal and economic development.

This timely study examines predictor mechanisms of innovation in retail banking that resonate within the restrictions of organisational and DI frameworks and the social exchange theory. Exploring the intervening effect of TE in the leadership, culture and innovation associations is unprecedented.

This paper offers a definition of the core of information science, which encompasses most research in the field. The definition provides a unique identity for information science and positions it in the disciplinary universe.

After motivating the objective, a definition of the core and an explanation of its key aspects are provided. The definition is related to other definitions of information science before controversial discourse aspects are briefly addressed: discipline vs. field, science vs. humanities, library vs. information science and application vs. theory. Interdisciplinarity as an often-assumed foundation of information science is challenged.

Information science is concerned with how information is manifested across space and time. Information is manifested to facilitate and support the representation, access, documentation and preservation of ideas, activities, or practices, and to enable different types of interactions. Research and professional practice encompass the infrastructures – institutions and technology –and phenomena and practices around manifested information across space and time as its core contribution to the scholarly landscape. Information science collaborates with other disciplines to work on complex information problems that need multi- and interdisciplinary approaches to address them.

The paper argues that new information problems may change the core of the field, but throughout its existence, the discipline has remained quite stable in its central focus, yet proved to be highly adaptive to the tremendous changes in the forms, practices, institutions and technologies around and for manifested information.

The study directs attention to the psychological conditions experienced and knowledge management practices leveraged by faculty in higher education institutes (HEIs) to cope with the shift to emergency remote teaching caused by the COVID-19 pandemic. By focusing attention on faculty experiences during this transition, this study aims to examine an under-investigated effect of the pandemic in the Indian context.

Interpretative phenomenological analysis is used to analyze the data gathered in two waves through 40 in-depth interviews with 20 faculty members based in India over a year. The data were analyzed deductively using Kahn’s framework of engagement and robust coding protocols.

Eight subthemes across three psychological conditions (meaningfulness, availability and safety) were developed to discourse faculty experiences and challenges with emergency remote teaching related to their learning, identity, leveraged resources and support received from their employing educational institutes. The findings also present the coping strategies and knowledge management-related practices that the faculty used to adjust to each discussed challenge.

The study uses a longitudinal design and phenomenology as the analytical method, which offers a significant methodological contribution to the extant literature. Further, the study’s use of Kahn’s model to examine the faculty members’ transitions to emergency remote teaching in India offers novel insights into the COVID-19 pandemic’s effect on educational institutes in an under-investigated context.

The competition in the academe has always been tough, but today, the academe seems to be more like an industry than an academic community as academics are evaluated through quantified and economic means.

This article leans on Heidegger’s thoughts on the essence of technology and his ontological view on being to show the dangers that lie in this quantification of researchers and research.

Despite the benefits that information systems (ISs) offer to people and research, it seems that technology has made it possible to objectify researchers and research. This has a negative impact on the academe and should thus be looked into especially by the IS field, which should note the problems that exist in its core. This phenomenon of quantified academics is clearly visible at academic quantification sites, where academics are evaluated using metrics that count their output. It seems that the essence of technology has disturbed the way research is valued by emphasising its quantifiable aspects. The study claims that it is important to look for other ways to evaluate researchers rather than trying to maximise research production, which has led to the flooding of articles that few have the time or interest to read.

This paper offers new insights into the current phenomenon of quantification of academics and underlines the need for critical changes if in order to achieve the academic culture that is desirable for future academics.