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Previous studies have rarely integrated the financing modes of a capital-constrained manufacturer with the choices of online sales strategies. To address this gap, the authors study how a manufacturer selects optimal financing modes under different sales strategies in three dual-channel supply chains.

This paper considers three sales strategies, namely, combining a traditional retailer channel with one of the direct selling, reselling and agency selling channels, and two common financing modes, namely, bank financing and retailer financing. The authors obtain equilibrium outcomes of the manufacturer and traditional retailer and then provide the conditions for them to select optimal financing modes under three sales strategies.

The results indicate that the manufacturer’s financing decisions rely on the initial capital and interest rates, and the manufacturer selects retailer financing only if the initial capital is relatively larger. In terms of financing mode options, the retailer financing mode is more beneficial for the manufacturer under the three sales strategies. From the perspective of sales strategies, the direct selling model is more beneficial. In addition, the higher the consumer acceptance of the online channel, the more profits the manufacturer obtains.

This paper provides suggestions on how the capital-constrained manufacturer chooses financing modes and sales strategies.

This paper integrates the financing mode and different sales strategies to investigate the manufacturer’s optimal operational decisions. These sales strategies allow us to investigate the manufacturer’s optimal financing modes in the presence of both different financing modes and sales strategies.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This study aimed to examine beliefs in repressed memory and dissociative amnesia from a cross-cultural perspective.

Chinese (n = 123) and Belgian student participants (n = 270) received several statements tapping into various dimensions of repressed memory and dissociative amnesia. Participants provided belief ratings for each of these statements. Because the field of psychoanalysis is less well developed in China, it was expected that Chinese participants would believe less in repressed memory and dissociative amnesia than their Belgian counterparts.

Overall, beliefs in repressed memory and dissociative amnesia were high among all participants. Although confirmatory analyses revealed that most belief ratings concerning statements did not statistically significantly differ between the two samples, Chinese participants did statistically believe less that therapy can recover lost traumatic memories than Belgian participants. Also, exploratory analyses showed that Chinese participants were more critical towards the idea that traumatic memories can be unconsciously repressed and that these memories can be accurately retrieved in therapy than Belgian participants. Many participants also confused repressed memory with plausible memory mechanisms such as ordinary forgetting.

The current study extends previous surveys on repressed memory and dissociative amnesia by comparing their beliefs in different cultures.

Urbanization in Sub-Saharan Africa (SSA) has seen rapid growth in recent decades, resulting in significant changes to the region’s landscape and ecosystems, including wetlands. This study aims to examine the causes and effects of urbanization on wetlands. This study lays down the need to intervene to protect and restore wetlands in SSA.

The methodology used was a systematic literature review, supported by the VOSviewer software and the preferred reporting items for systematic reviews criteria, with data analyzed using abductive reasoning and content analysis.

This study found that a complex web of factors reflecting regional and global trends propels urbanization in Sub-Saharan Africa. Urbanization is driven by population growth, economic development, infrastructure development and migration, leading to significant changes in the region’s ecosystems. The key effects include biodiversity loss, flooding and altered hydrology, water quality degradation and loss of livelihood. The study identifies sprawling urbanization, densification, informal settlement, fragmented urbanization and planned urban expansion as patterns of urbanization affecting wetlands.

This study offers practical recommendations for policymakers, planners and local communities to ensure long-term urban sustainability while conserving wetland ecosystems in SSA. Thus, there is a need for continued cooperation, technology and discovery sharing, and cooperative research funding initiatives with the global community. It also commends implementing green infrastructure, like artificial wetlands, to mitigate the adverse environmental effects and promote sustainable development.

This study used VOSviewer software visualization to uncover structural trends and research frontiers, focusing on wetland conservation in the context of urban areas in SSA, where rapid urbanization adds to wetland degradation.

As it is known, the accounting information system (AIS) plays a significant role in the business ecosystem by recording and processing financial and accounting data and reporting the produced information to all relevant parties for decision-making. However, its used methods and systems, including double-entry bookkeeping and enterprise resource planning (ERP) systems, have limitations, especially in terms of trust and reliability concerns for stakeholders and the possible scope for records manipulation and fraud. The application of blockchain technology is believed to enhance the reliability of the AIS and addresses many of its current limitations. Blockchain can offer numerous benefits if used to manage AIS functions through enhanced trust, reliability, and transparency, increased efficiency, reduced costs and fraud, improved accounting information quality and real-time accounting. Nevertheless, the adoption and implementation of blockchain in the AIS are associated with several technical and nontechnical challenges which are not easy to address and could limit the wide technology adoption in the immediate future. Considering that a full understanding of the benefits and challenges of adopting blockchain in the AIS still needs more clarification, this chapter examines blockchain technology and its implications for the AIS. It reviews blockchain characteristics and its benefits to the AIS, discusses its possible integration into the AIS, outlines adoption and implementation challenges, and suggests critical avenues for future research.

This study aims to comprehensively analyze the current developments and applications of paper-based electrochemical platforms for blood glucose detection, focusing on their potential to revolutionize point-of-care testing through cost-effective and accessible diagnostic solutions.

The review systematically examines fundamental principles of paper-based platforms, including substrate properties, fluid transport mechanisms and electrochemical detection methods. It critically evaluates recent technological advances in materials science, fabrication techniques and signal amplification strategies while analyzing various case studies demonstrating successful implementations.

Recent innovations in paper-based glucose sensors have achieved remarkable performance metrics, with detection limits reaching sub-millimolar ranges and response times within seconds. The integration of nanomaterials, particularly graphene-based composites and carbon nanotubes, has significantly enhanced sensor sensitivity and stability. Advanced enzyme immobilization techniques using layer-by-layer assembly have demonstrated sustained activity for up to 10 weeks, while novel signal amplification strategies incorporating bimetallic nanoparticles have pushed detection limits into the sub-picogram range.

This review uniquely synthesizes the latest developments in paper-based electrochemical glucose sensing, providing critical insights into the synergistic integration of advanced materials, fabrication methods and detection strategies. It offers valuable perspectives on overcoming current technical challenges and highlights emerging opportunities in smart device integration and artificial intelligence applications, serving as a comprehensive resource for researchers and practitioners in the field of point-of-care diagnostics.

This study aims to examine the combinations of internal and external knowledge flows between research and development (R&D) incumbents and start-ups in the context of open innovation. While there is a growing body of knowledge that has examined how, in a knowledge economy, a firm’s knowledge and innovation activities are closely linked, there is no systematic review available of the key antecedents, perspectives, phenomenon and outcomes of knowledge spillovers.

The authors have conducted dual-stage research. First, the authors conducted a systematic review of literature (97 research articles) by following the theories–contexts–methods framework and the antecedent-phenomenon-outcomes logic. The authors identified the key theories, contexts, methods, antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. In the second stage, the findings of stage one were leveraged to advance a nomological network that depicts the strength of the relationship between the observable constructs that emerged from the review.

The findings demonstrate how knowledge spillovers can help incumbent organisations and start-ups to achieve improved innovation capabilities, R&D capacity, competitive advantage and the creation of knowledge ecosystems leading to improved firm performance. This study has important implications for practitioners and managers – it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. The emerging network showed that the antecedents of knowledge spillovers have a direct relationship with the creation of a knowledge ecosystem orchestrated by incumbents and that there is a very strong influence of knowledge capacities and knowledge types on the selection of external knowledge partners/sources.

This study has important implications for practitioners and managers. In particular, it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. This will enable managers to take important decisions about what knowledge capacities are required to achieve innovation outcomes. The findings suggest that managers of incumbent firms should be cautious when deciding to invest in knowledge sourcing from external partners. This choice may be driven by the absorptive capacity of the incumbent firm, market competition, protection of intellectual property and public policy supporting innovation and entrepreneurship.

Identification of the key antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. The findings from Stage 1 helped us to advance a nomological network in Stage 2, which identifies the strength and influence of the various observable constructs (identified from the review) on each other. No prior study, to the best of the authors’ knowledge, has advanced a nomological network in the context of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context.

This study proposes to conceptualize and explicate the progress of end-to-end data pipelines to enable seamless data flow, addressing the mounting demand for instantaneous information in the vibrant field of marketing analytics. Ensuring timely access to inclusive analytics, it empowers marketers to make conversant decisions promptly. The study espouses a conceptual approach, starting with the classification and identification of various data sources crucial for thorough marketing analytics. It probes into the complexities of designing adaptable and scalable architectures for extensive data pipelines, ensuring effective data collection, processing, and storage. Emphasizing case analyses of marketing analytics tools, the study delivers practical insights and exhaustive examinations of existing solutions. The findings offer a broad understanding of the key components and strategies necessary for realizing smooth data flow in real-time marketing analytics. This investigation contributes expressively to the field by outlining a robust framework for continuous data flow, identifying the benefits and challenges associated with data pipelines. From a hands-on standpoint, it provides organizations with appreciated insights for building effective end-to-end data pipelines, bestowing strategies for implementing real-time marketing analytics outlines that enhance decision-making processes. The originality and value of this research lie in its universal approach to addressing the challenges of real-time marketing analytics, offering a fresh viewpoint on creating seamless data flow frameworks. The gathered information has the latent to drive innovation in marketing analytics, serving as a crucial resource for companies aiming to gain a competitive edge in today’s data-driven market. This inclusive approach ensures that the study’s contributions are both theoretically significant and practically applicable.

This study aims to investigate the impact of viewing different types of digital environment images on college students’ positive emotions, nature relatedness and environmental preference. This aimed to address the gap in empirical studies regarding the effects of digital environment images on these psychological constructs.

This study used a three-group experimental between-subjects design. Participants (48 undergraduate students) were divided into three groups, each viewing a different set of digital images (nature, city or object). Pre- and posttest measures assessed positive emotions and nature relatedness, whereas environmental preference was measured after image viewing. One-way analysis of variance and post hoc Tukey’s honestly significant difference (HSD) tests were used to analyze the data.

Viewing digital nature images elicited significantly higher positive emotions, nature relatedness and environmental preference compared to viewing city or object images. In addition, environmental preference for the digital object group was marginally higher than the city group.

This study was limited by its relatively small sample size. Although further research is needed to investigate the underlying mechanisms behind the observed effects, this study provides valuable implications for education, economic dimensions and public policy initiatives, encouraging the development of pro-environmental attitudes and behaviors.

The findings suggest that incorporating digital nature images into learning activities can promote positive emotions, nature relatedness and environmental preference among college students. This has implications for the design of digital learning environments, especially for those with limited access to natural environments.

By promoting positive emotions and nature relatedness, digital nature experiences can contribute to emotional well-being and potentially foster pro-environmental behaviors. This can have implications for promoting sustainable lifestyles and environmental conservation efforts.

This study provides original empirical evidence on the impact of viewing digital environment images on several psychological constructs. It highlights the potential of digital nature experiences as a complement to direct nature exposure, particularly for those with limited access to natural environments. These findings contribute to the growing body of literature on the benefits of digital nature experiences and have implications for various fields, including education, environmental psychology and human–computer interaction.

The purpose of the review is to synthesize the research on materiality measures of sustainability reporting and highlight how preparers, users, auditors, regulators and other stakeholders assess or determine the materiality in sustainability reporting. The review further summarizes the findings on consequences and determinants of material disclosures in sustainability reporting. Several directions for future research are also discussed.

This study provides a systematic review of materiality measures developed in the context of sustainability reporting. This synthesis of the literature summarizes the existing methodologies of measuring materiality. It also evaluates the strength and limitations of existing methods and approaches of measuring materiality in sustainability disclosures.

We find that the ex post materiality measures are simplistic and unidirectional in nature and ex ante materiality measures lack external validity and are generally narrow in focus – for example, focused on single firms or industries. Another major limitation in the current literature is the absence of robust empirical investigation of double materiality in sustainability reporting and a vast majority of the measures are developed without stakeholder engagement. Lastly, we document that the findings on determinants of material disclosure are fragmented and inconclusive and that the literature on consequences of material disclosure is rather un-explored.

The study explains the connections and differences between the various materiality measures. We document that materiality is measured in two distinct ways, ex ante and ex post and often times without stakeholder engagement. Moreover, given that a vast majority of the measures rely on manual content analysis, we find that they suffer from reproducibility and scalability.