Search results

The purpose of this paper is to present a conceptual model of guanxi‐oriented human resource management (HRM) appropriate for organisations located in China that wish to maximise knowledge sharing and exchange. The recognised need by Western‐based organisations to develop trusting relationships through appropriate HRM approaches presents the setting to introduce the importance of guanxi in Chinese culture. The added complexity for Chinese organisations that employ a diversity of what are termed, in this paper, Conventional and Foreign Educated Chinese and Acculturated and Newly Arrived Expatriates is also presented.

Building on a review of literature about what challenges the knowledge revolution is having on the way that Western organisations manage their human resources, the paper presents an overview of the notion and function of guanxi in the Chinese context. The paradoxical and ambiguous nature of guanxi and its implications for interpersonal interactions are explained as being important for HR practitioners to understand.

The paper presents a conceptual guanxi‐oriented HRM model that incorporates the Chinese mindset that is, at one and the same time, paradoxical, holistic, dynamic, interdependent, flexible and reciprocal in nature.

While conceptual, the paper is based on a model developed from research by one of the authors that identified a guanxi‐oriented model of conflict resolution. The paper acknowledges that this model requires further research to test its applicability.

The paper is original in its attempt to link guanxi to HRM practices on Western concepts of human resources.

Drawing upon the stimulus–organism–response (S-O-R) model, the purpose of this study is to examine how perceptions of young customers towards the green image of trendy coffee cafés affect their environmental and product attitudes, and subsequently their citizenship behaviour as well as intention to re-patronage. The mediating effect of customer citizenship behaviour (CCB) is also assessed.

The instrument was developed by adapting measurement from the past studies. Using the purposive sampling technique, data were collected online from 207 young customers in Malaysia who frequented the cafés. Partial least squares structural equation modelling (PLS-SEM) was used to perform path modelling and mediation analyses.

The findings show that green image stimulates both customers’ environmental attitude and product attitude. Although product attitude is found to have a dominant effect on CCB, the impact of environmental attitude on CCB and re-patronage intention is worth noting. Moreover, advocacy and tolerance significantly mediate the relationship between product attitude and re-patronage intention.

This study advances the consumer behaviour literature by determining the influence of green image on two forms of attitudes as well as the mediating role of the multi-dimensional CCB between attitudes and intention to re-patronage trendy coffee cafés among young customers. While the findings confirm the importance of product attitude and the relevance of advocacy and tolerance in relation to re-patronage, the study also highlights the growing awareness of green image among young customers and its implications on knowledge and practice.

Corporate digital transformation (CDT) has challenged traditional tax administration systems. This study examines the impact of CDT on tax avoidance behavior and tests whether tax authorities can identify this behavior.

Using data on listed companies on the Shanghai and Shenzhen Stock Exchanges from 2008 to 2020, this study applies the Heckman two-stage and cross-section models.

The results show that the higher the degree of CDT, the more aggressive the tax avoidance behavior. The CDT's impact on corporate tax avoidance is more significant under strong government tax efforts.

This study expands research on the economic consequences of CDT and the factors influencing corporate tax avoidance behavior. Moreover, it has important implications for governments to monitor tax avoidance behavior under the CDT, improve digital tax systems, and pay more attention to the tax administration of digital assets.

The purpose of this paper is to present work which is a part of the Comprehensive Automation for Specialty Crops project (CASC). Desired trajectory tracking objective has been previously performed by using a non‐model based approach in this project. Long distance autonomous drive has been achieved; however the results haven't met the expectations of the project requirements. In order to provide these requirements, this study is conducted. In this study, long distance autonomous trajectory tracking for an orchard vehicle is studied. Besides longitudinal motion, lateral motion of the vehicle is also considered. The longitudinal and lateral errors are objected to keep into a region of less than 10 cm.

Car‐like robot kinematic modeling approach is used to create desired trajectory. In order to control longitudinal velocity and steering angle of the vehicle, a controller methodology is proposed. Stability of the controller proposed is shown by using Lyapunov stability approach.

The proposed model is adapted into a four‐wheeled autonomous orchard vehicle and tested in an experimental orchard for long distance autonomous drives. More than 15 km autonomous drive is successfully achieved and the details are presented in this paper.

In this study, long distance autonomous trajectory tracking for an orchard vehicle is focused. A model based control strategy, including the information about longitudinal and lateral motion of the vehicle, is constructed. A new approach to create steering angles for turning operations of the orchard vehicle is introduced. It is objected that the longitudinal and lateral errors should be less than 10 cm during the trajectory tracking task.

This paper provides a structured literature review of blockchain in accounting. The authors identify current trends, analyse and critique the key topics of research and discuss the future of this nascent field of inquiry.

This study’s analysis combined a structured literature review with citation analysis, topic modelling using a machine learning approach and a manual review of selected articles. The corpus comprised 153 academic papers from two ranked journal lists, the Association of Business Schools (ABS) and the Australian Business Deans Council (ABDC), and from the Social Science Research Network (SSRN). From this, the authors analysed and critiqued the current and future research trends in the four most predominant topics of research in blockchain for accounting.

Blockchain is not yet a mainstream accounting topic, and most of the current literature is normative. The four most commonly discussed areas of blockchain include the changing role of accountants; new challenges for auditors; opportunities and challenges of blockchain technology application; and the regulation of cryptoassets. While blockchain will likely be disruptive to accounting and auditing, there will still be a need for these roles. With the sheer volume of information that blockchain records, both professions may shift out of the back-office toward higher-profile advisory roles where accountants try to align competitive intelligence with business strategy, and auditors are called on ex ante to verify transactions and even whole ecosystems.

The authors identify several challenges that will need to be examined in future research. Challenges include skilling up for a new paradigm, the logistical issues associated with managing and monitoring multiple parties all contributing to various public and private blockchains, and the pressing need for legal frameworks to regulate cryptoassets.

The possibilities that blockchain brings to information disclosure, fraud detection and overcoming the threat of shadow dealings in developing countries all contribute to the importance of further investigation into blockchain in accounting.

The authors’ structured literature review uniquely identifies critical research topics for developing future research directions related to blockchain in accounting.

This study aims to examine the combined impact of environmental, social and governance (ESG) ratings on the market and financial performance of Egyptian companies during the period from 2007 to 2016 and, thereby, determines the influence of the recent political revolutions –that broke out in the MENA region in early 2011 – on the association between ESG practices and corporate performance.

The present work uses data from the S&P/EGX ESG index, which is the first of its kind in the MENA region. The ESG index is designed to increase the profile of companies listed on the Egyptian Exchange and is expected to boost the level and quality of ESG practices in the Egyptian context. The sample includes the 100 most active Egyptian companies in the Egyptian Stock Exchange as measured by the EGX 100 index in the financial year that ended in 2016. The sample begins in 2007, concurrent with the start of the ESG index, and ends in 2016. The period from 2007 to 2010 represents the pre-revolution period, and the period from 2012 to 2016 is the post-revolution period.

Firms with high ESG ratings are found to enjoy a better financial and market performance. The authors found some evidence that the influence of ESG ratings on financial performance is more obvious after the revolutions than before the revolutions.

This study provides insights regarding the impact of political events on the market in the Middle East region. Despite its increasing economic and political importance, this region still suffers from inadequate attention in the literature. The present work investigates the variances that evolved out of the events that started in early 2011 and the implications of these events on the market. The results of this study have implications for regulators and investors in the Egyptian stock market. The authors believe that the relatively new S&P/EGX ESG index provides a way to enhance ESG ratings in Egypt.

The results of the present study provide insights for policymakers regarding the usefulness of the sustainability indices.

The present results contribute to the growing literature on the economic consequences of ESG ratings, especially in relation to a context characterized by intense political/revolutionary changes. In particular, this study contributes to the few works that have addressed the economic implications of ESG ratings in emerging markets.

The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method.

A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed.

The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm.

MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.

The aim of this paper is to assess the safety level of construction sites in Afghanistan following the US Army Corps of Engineers (USACE) safety practices and compare this with other construction sites in Afghanistan that follow local government safety guidelines. The USACE oversees and funds many infrastructure projects in Afghanistan, and these projects are supposed to follow the same standards implemented in the USA, including safety standards. The local government of Afghanistan also funds infrastructure projects in Afghanistan; however, these do not follow USACE best practices. This research explores the question of whether the USACE standards provide a safer construction site. The effect of the USACE standards on safety practices in construction projects is also investigated in the Afghanistan construction industry.

A literature review and other safety checklists were used to develop a safety checklist containing 104 items (questions) in 17 categories. Subsequently, the checklist was used to assess the safety performance of 57 construction projects (25 USACE projects, and 32 governmental projects). Utilizing quantitative analysis, the Spearman rank correlation coefficient (Rho) and Mann–Whitney tests were carried out for correlation and statistical disparity between USACE and governmental projects.

The safety performance level of Afghan government projects was found to be poor in relation to other developing countries, while in USACE projects it was excellent. In addition, fire prevention, safety administration, PPE, heavy equipment, and handling and storage of materials for all types of contractors were the most overlooked aspects of Afghan Government projects.

The findings clearly demonstrate the deficiencies in construction sites observed during this study and also support the adoption of USACE standards in Afghanistan projects.

To the best of the authors’ knowledge, this study is the first to investigate the safety of construction sites in Afghanistan. The study also demonstrates the benefits of adopting international standards (USACE) to improve the safety of construction sites in a developing country such as Afghanistan. The findings provide evidence of the safety of the Afghanistan construction industry compared to other developing countries. These findings will contribute to the Afghan Government's efforts to track injury statistics.

This paper aims to expand the emerging field of symbolic green consumer behavior (GCB) by investigating the impact of anticipated conspicuousness of the consumption situation on consumers’ choice of organic products. In addition, the paper also explores whether self-monitoring ability and attention to social comparison information (ATSCI) influence GCB in situations of anticipated high conspicuousness.

Two experiments test the study’s hypotheses.

The results of both experiments show that the anticipation of conspicuousness has a significant effect on GCB. Moreover, in Experiment 2, this effect is moderated by consumers’ level of ATSCI but not by their self-monitoring ability.

Because ATSCI significantly interacts with green consumption because of the anticipation of a conspicuous setting, although self-monitoring ability does not, we conclude that social identification is an important determinant of green consumption.

Marketers who focus on building green brands could consider designing conspicuous consumption situations to increase GCB.

Policymakers could enact change by making the environmental unfriendliness of non-eco-friendly products visible to the public and thus increase the potential for GCB.

The results validate the emerging understanding that green products are consumed for self-enhancement, but also expand the literature by highlighting that a key motivating factor of GCB is the desire to fit in.

The purpose of this paper is to improve the control precision of the station-keeping control for a stratosphere airship through the feedforward-feedback PID controller which is designed by the wind speed prediction based on the incremental extreme learning machine (I-ELM).

First of all, the online prediction of wind speed is implemented by the I-ELM with rolling time. Second, the feedforward-feedback PID controller is designed through the position information of the airship and the predicted wind speed. In the end, the one-dimensional dynamic model of the stratosphere airship is built, and the controller is applied in the numerical simulation.

Based on the conducted numerical simulations, some valuable conclusions are obtained. First, through the comparison between the predicted value and true value of the wind speed, the wind speed prediction based on I-ELM is very accurate. Second, the feedforward-feedback PID controller designed in this paper is very effective.

This paper is very valuable to the research of a high-accuracy station-keeping control of stratosphere airship.