Search results

A flexible decision technology called the Graph Model for Conflict Resolution (GMCR) is applied to a generic aquaculture conflict to illustrate how GMCR can be used to systematically investigate a wide range of conflicts arising in aquaculture in order to obtain meaningful strategic insights and thereby assist in making informed decisions in aquaculture development. To emphasize the importance of being able to resolve aquaculture controversies, a review of the global economic impacts of the aquaculture industry is provided and the key stakeholders who may be involved in aquaculture disputes along with their legitimate interests are identified. The paper aims to discuss these issues.

The GMCR methodology comprises two main stages: modeling and analysis. During the modeling stage, key decision makers (DMs), the options under each DM’s control and each DM’s relative preferences over feasible states are identified based on a thorough background investigation to a given dispute. Within the analysis stage, solution concepts that describe key characteristics of human behavior under conflict are utilized to determine resolutions that could occur when DMs interact under pure competition and cooperatively. Interpretation of the equilibrium results provides meaningful strategic insights for better understanding which strategies a given DM could select as the conflict evolves over time.

The results demonstrate how difficult it can be to balance the interests of different key stakeholders in aquaculture development. In all possible resolutions identified in the generic aquaculture conflict, at least two DMs among First Nations, environmental group and residents (Res) would object to the expansion of aquaculture activities due to the assumption that the government would choose to appease one stakeholder at a time. They also reflect the need for a useful tool box of decision technologies for addressing the vast range of challenges that could arise in the important area of marine economics and management.

The GMCR methodology possesses several unique and key original capabilities in comparison to other conflict analysis models. First, it only requires limited information to calibrate a conflict model. Second, it contains a number of solution concepts that describe how a DM could think and behave under conflict. Third, it furnishes a range of informative output, follow-up analyses and advice for use in real-life decision support. Finally, all of the foregoing advantages of GMCR can be contained within decision support systems that permit practitioners and researchers to readily apply the GMCR methodology to real-life conflicts.

As financial markets for environmentally friendly investment grow in both scope and size, analyzing the relationship between green financial markets and African stocks becomes an important issue. Therefore, this paper examines the role of infectious disease-based uncertainty on the dynamic spillovers between African stock markets and clean energy stocks.

The authors employ the dynamic spillover in time and frequency domains and the nonparametric causality-in-quantiles approach over the period of November 30, 2010, to August 18, 2021.

These findings are discernible in this study's analysis. First, the authors find evidence of strong connectedness between the African stock markets and the clean energy market, and long-lived but weak in the short and medium investment horizons. Second, the BDS test shows that nonlinearity is crucial when examining the role of infectious disease-based equity market volatility in affecting the interactions between clean energy stocks and African stock markets. Third, the causal analysis provides evidence in support of a nonlinear causal relationship between uncertainties due to infectious diseases and the connection between both markets, mostly at lower and median quantiles.

Considering the global and recent use of clean energy equities and the stock markets for hedging and speculative purposes, one may argue that rising uncertainties may significantly influence risk transmissions across these markets. This study, therefore, is the first to examine the role of pandemic uncertainty on the connection between clean stocks and the African stock markets.

The wake of the novel coronavirus (COVID-19) pandemic had caused substantial disruptions to the usual delivery of healthcare services. This is because of restrictive orders that were put in place to curb the spread of the infection. Palliative care services in Brunei also face challenges to deliver effective services during this period. However, the impact of advanced illnesses on patients' health and end-of-life care are issues that cannot be planned, postponed or cancelled. Hence, the palliative care team needs to continue to deliver effective palliative care services. As Brunei faced its second pandemic wave in August 2021, crucial adaptations were made to ensure palliative care service was not disrupted. This reflective case study aims to discuss the adaptations made in providing palliative care during this era of disruptions.

Advanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.

A random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.

The acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.

Few research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.

Japan's decision to release nuclear wastewater into the Pacific Ocean in 2023 has sparked strong opposition at home and abroad. In this study, Graph Model for Conflict Resolution (GMCR) method is adopted to analyze the conflict problem, and reasonable equilibrium solutions are given to solve the conflict event.

In this study, GMCR is adopted to solve the conflict problem. First, identify the key decision-makers (DMs) on the issue of nuclear effluent and the relevant options they might adopt. Second, the options of each DM are arranged and combined to form a set of feasible states. Thirdly, the graph model is constructed according to the change of DM's options, and the relative preference of each DM is determined. Finally, the conflict problem is solved according to the definition of GMCR equilibrium.

Discharging nuclear wastewater into the ocean is not the right choice to solve the problem. Developing more space to store nuclear wastewater is more conducive to the protection of the ocean environment.

It is undesirable for the Japanese government to unilaterally discharge nuclear wastewater into the ocean. Objectively assessing the radioactivity of nuclear wastewater and the cooperation of relevant stakeholders can better solve this conflict.

The problem arising from Japan's releasing plan is complicated because of a lack of information and the existence of multiple stakeholders, while GMCR can help us with a better view of the current circumstance in the conflict.

This study investigates whether corporate executives, who are university alumni, influence each other's firm corporate social responsibility (CSR) performance.

Drawing on social network theory, the authors hypothesise that a firm's CSR performance is positively associated with its peer firms' average CSR performance when the executives of the firm and its peer firms are university alumni. The study employs data from 1,685 listed firms and 4,906 executives who graduated from 585 different universities in China and runs multivariate regressions.

The results reveal a sizeable university peer influence on CSR performance. Such influence is even stronger for executives who graduated from elite universities (e.g. 985 or 211 universities), and universities or programmes that provide more opportunities for alumni reunions or networking (e.g. MBAs/EMBAs). Executives who are more influential in making firm decisions (e.g. CEOs/CFOs), as well as firms that are more likely to mimic the behaviour of others, also show higher degrees of university peer influence.

The results highlight the role of education in ethical decision-making.

This study documents evidence on a new determinant of firm CSR performance. The study sheds light on the impact of non-institutionalised personal ties, for example, university alumni networks, on CSR performance.

Social media has become an inescapable part of our lives. However, recent research suggests that excessive use of social media may lead to fatigue and users’ disengagement. This study aims to examine which brand-related factors contribute to social media fatigue (SMF) and its subsequent role on driving lurking behaviors, particularly among young consumers.

Based on survey data from 282 young users of social media, a holistic model of brand-related drivers and outcomes of SMF was tested, emphasizing the contribution of brands’ social media presence to users’ disengagement.

Research shows that branded content overload and irrelevance, as well as branded ads intrusiveness significantly impact SMF, which in turn plays a mediating role between brand-related drivers and lurking behaviors. The authors further conclude that the impact of SMF on lurking is stronger for users who follow a larger set of brands.

The study contributes to social media research by addressing its “dark side” and empirically validating the role of brands’ social media presence in developing young users’ fatigue and disengagement. The study further adds to the scant literature on SMF, which was mostly developed outside the branding field. Research also provides valuable insights to brands on how to improve their social media performance.

Knowledge sharing has entered the stage of knowledge payment with the typical models of paid Q&A, live session, paid subscription, course column and community service. Numerous knowledge suppliers have begun to pour into the knowledge payment market, and users' willingness to pay for premium content has increased. However, the academic research on knowledge payment has just begun.

In this paper, the authors searched several bibliographic databases using keywords such as “knowledge payment”, “paid Q&A”, “pay for answer”, “social Q&A”, “paywall” and “online health consultation” and selected papers from aspects of research scenes, research topics, etc. Finally, a total of 116 articles were identified for combing studies.

This study found that in the early research, scholars paid attention to the definition of knowledge payment concept and the discrimination of typical models. With the continuous enrichment of research literature, the research direction has gradually been refined into three main branches from the perspective of research objects, i.e. knowledge provider, knowledge demander and knowledge payment platform.

This paper focuses on discussing and sorting out the key research issues from these three research genres. Finally, the authors found out conflicting and contradictory research results and research gaps in the existing research and then put forward the urgent research topics.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.