Purpose
The purpose of this study is to investigate how digital technologies are used for facilitating knowledge sharing and decision-making through enhanced coordination and communication and their ...impact on job performance in organizations.
Design/methodology/approach
A conceptual model is developed within the background of the social capital theory through a comprehensive review of the related literature for exploring how digital technologies can improve knowledge sharing and decision-making via enhanced communication and coordination between individuals in organizations for better job performance. This model is then tested and validated based on structural equation modeling of the collected survey data in Australia.
Findings
This study shows that digital technology enhanced coordination and communication have significant impact on knowledge sharing. It finds out that digital technology driven coordination significantly influences decision-making and digital technology driven knowledge sharing significantly influences decision-making. Furthermore, this study reveals that enhanced decision-making and knowledge sharing can lead to better job performance in organizations.
Originality/value
To the best of the authors’ knowledge, this study is the first attempt to explore the role of digital technologies in enhancing knowledge sharing and decision-making for better job performance in a digitalized working environment in organizations. The validated model can be used as the foundation to further investigate the changing role of digital technologies in driving knowledge sharing for better performance of individuals and competitive advantages of organizations.
The coronavirus (COVID-19) pandemic has created a global medical emergency. The unforeseen occurrence of a pandemic of this magnitude has resulted in overwhelming levels of medical waste and raises ...questions about management and disposal practices, and environmental impacts. The amount of medical waste generated from COVID-19 since the outbreak is estimated to be 2.6 million tons/day worldwide. In Australia, heaps of single-use gowns, facemasks/face shields, aprons, gloves, goggles, sanitizers, sharps, and syringes are disposed everyday as a result of the pandemic. Moreover, the establishment of new home/hotel quarantine facilities and isolation/quarantine centres in various Australian states and territories have increased the risks of transmission among people in these facilities and the likelihoods of general waste becoming contaminated with medical waste. This warrants the need to examine management and disposal practices implemented to reduce the transmission and spread of the virus. This study reviews the various management and disposal practices adopted in Australia for dealing with medical waste from the COVID-19 pandemic and their impacts on public health and the environment. To achieve the aims of this study, prior studies from 2019-2021 from various databases are collected and analysed. The study focuses on generation of medical waste from COVID-19, management and disposal methods, current problems/challenges and environmental and public health impacts. Considering the enormous risks involved and the significance of appropriate handling and disposal of medical waste from COVID-19, this study provides insights on short and long term responses towards managing COVID-19 waste in Australia. The study contributes to Australia's efforts against the transmission and spread of COVID-19 and provides recommendations for the development of workable and sustainable strategies for mitigating similar pandemics in the future.
Due to the rapid increase in the use of electrical and electronic equipment (EEE) worldwide, e-waste has become a critical environmental issue for many governments around the world. Several studies ...have pointed out that failure to adopt appropriate recycling practices for e-waste may cause environmental disasters and health concerns to humans due to the presence of hazardous materials. This warrants the need for a review of the existing processes of e-waste management. In view of the growing e-waste generation in the Asia Pacific region and the importance of e-waste management, this study critically reviews previous research on e-waste generation and management practices of major e-waste producing nations (Australia, China, India, Indonesia, and Malaysia) in the Asia Pacific region, provides an overview of progress made and identifies areas for improvement. To fulfil the aims of this research, previous studies from 2005 to 2020 are collected from various databases. Accordingly, this study focuses on e-waste generation and environmental management of these countries. This study found that e-waste management practices of the selected countries need to be enhanced and recommends several best practices for effectively managing e-waste.
Deep learning has emerged as a potential tool for crop yield prediction, allowing the model to automatically extract features and learn from the datasets. Meanwhile, smart farming technology enables ...the farmers to achieve maximum crop yield by extracting essential parameters of crop growth. This systematic literature review highlights the existing research gaps in a particular area of deep learning methodologies and guides us in analyzing the impact of vegetation indices and environmental factors on crop yield. To achieve the aims of this study, prior studies from 2012 to 2022 from various databases are collected and analyzed. The study focuses on the advantages of using deep learning in crop yield prediction, the suitable remote sensing technology based on the data acquisition requirements, and the various features that influence crop yield prediction. This study finds that Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) are the most widely used deep learning approaches for crop yield prediction. The commonly used remote sensing technology is satellite remote sensing technology—in particular, the use of the Moderate-Resolution Imaging Spectroradiometer (MODIS). Findings show that vegetation indices are the most used feature for crop yield prediction. However, it is also observed that the most used features in the literature do not always work for all the approaches. The main challenges of using deep learning approaches and remote sensing for crop yield prediction are how to improve the working model for better accuracy, the practical implication of the model for providing accurate information about crop yield to agriculturalists, growers, and policymakers, and the issue with the black box property.
The increasing use of digital technologies leads to more contingent, flexible, and distributed work arrangements between individuals and organizations, resulting in wide adoption of digital work. ...This makes understanding the impact of digital technologies on job performance critical. This study investigates the affordance of digital technologies for enhancing job performance in digital work. A technology affordance theory-based model is tested and validated through structural equation modeling of the collected survey data. The study shows that using digital technologies can enhance job performance through improved coordination, communication, knowledge sharing, and decision-making. This study is the first in applying the technology affordance theory for exploring the role of digital technologies in job performance in digital work. The findings can lead to better formulated strategies and policies to facilitate digital technology use.
PurposeDigital technologies have been transforming the traditional workplace and reshaping how work is designed, performed and managed in organizations. This makes understanding digital work and its ...impact on job performance critically important. This study investigates the impact of digital work on job performance in organizations from the perspective of technology affordance.Design/methodology/approachA comprehensive review of the related literature has been conducted, leading to the development of a conceptual model for exploring the impact of digital work on job performance from the perspective of technology affordance. Such a model is then tested and validated using structural equation modelling on the survey data collected in Australia.FindingsThe study shows that the use of digital technologies significantly improves coordination and knowledge sharing between individuals, leading to better work–life balance and improved job performance. Furthermore, the study reveals that the use of digital technologies that can enhance communication and decision-making does not significantly influence work–life balance and job performance in digital work.Originality/valueThis study presents a comprehensive investigation of the impact of digital work on job performance in organizations from the perspective of technology affordance. It explores the changing role of digital work in transforming existing working practices in organizations, and how different technology affordances in digital work can be actualized for improving work–life balance and job performance in a digitalized working environment.
PurposeThe purpose of this study is to examine the effects of consumer ethnocentrism and animosity on the importance of country-of-origin in food product evaluation. It also tested the moderating ...effect of purchase frequency.Design/methodology/approachData were collected from dairy consumers residing in China. The research model was tested using structural equation modelling with AMOS.FindingsThe results indicated that the importance of country-of-origin in product evaluation is not necessarily driven by consumer ethnocentrism or animosity. Only among frequent purchasers, a higher level of consumer ethnocentrism or animosity can be associated with more importance of country-of-origin in product evaluation.Originality/valueDespite the significant role of purchase frequency, this factor has been less considered in the existing literature on consumer ethnocentrism and animosity. This study represented an initial attempt to the role of purchase frequency in the effects of consumer ethnocentrism and animosity on food product evaluation. It revealed that purchase frequency should be adopted as a moderating factor in future studies in this field.
This paper explores the role of stakeholders’ trust in the adoption of smart city technologies, leading to the identification of the critical determinants for adopting smart city technologies in ...Australian regional cities. A comprehensive review of the related literature has been conducted. Such a review leads to the development of a trust-based research model for investigating the importance of trust in technology and its adoption. This model is then tested and validated with the use of a structural equation modeling technique on the survey data collected from ICT professionals in Australian regional cities. The study results show that perceived usefulness, perceived external pressure and perceived information security influence trust in smart city technologies. Further analysis highlights the significant relationship between stakeholders’ trust and their intention to adopt smart city technologies. This study is unique, as it is one of a few studies that focus on exploring stakeholders’ trust in the adoption of smart city technologies from the perspective of ICT professionals in Australia. The study results can be used by the government agencies to formulate appropriate policies to enhance the use of smart city technologies in the active pursuit of smart city development in Australia.
The volume of e-waste generated worldwide is surging, and it is set to escalate further due to continuing technological innovation and the early obsolescence of most electrical and electronic ...equipment (EEE). Even though there are many studies on e-waste management, the environmental and health consequences of e-waste regarding direct exposure during informal recycling and indirect exposure through environmental contamination are poorly studied. This study analyses the environmental and health consequences of e-waste dumping and informal recycling practices in selected countries such as Brazil, China, India, Mexico, and Pakistan. Several databases, such as Science Direct, ProQuest, Web of Science, and Emerald, were used to analyse studies from 2005 to 2022. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 179 journal articles were evaluated. This study found that the majority of e-waste is poorly managed in terms of ecological safety and soundness. This study also found that illegal dumping, acid leaching, and open burning, which are practices that harm the environment and the well-being of e-waste workers, are still being performed by the selected countries. This study provides several practical suggestions for addressing the environmental and health consequences of e-waste dumping and recycling.
User-generated multi-media content, such as images, text, videos, and speech, has recently become more popular on social media sites as a means for people to share their ideas and opinions. One of ...the most popular social media sites for providing public sentiment towards events that occurred during the COVID-19 period is Twitter. This is because Twitter posts are short and constantly being generated. This paper presents a deep learning approach for sentiment analysis of Twitter data related to COVID-19 reviews. The proposed algorithm is based on an LSTM-RNN-based network and enhanced featured weighting by attention layers. This algorithm uses an enhanced feature transformation framework via the attention mechanism. A total of four class labels (sad, joy, fear, and anger) from publicly available Twitter data posted in the Kaggle database were used in this study. Based on the use of attention layers with the existing LSTM-RNN approach, the proposed deep learning approach significantly improved the performance metrics, with an increase of 20% in accuracy and 10% to 12% in precision but only 12–13% in recall as compared with the current approaches. Out of a total of 179,108 COVID-19-related tweets, tweets with positive, neutral, and negative sentiments were found to account for 45%, 30%, and 25%, respectively. This shows that the proposed deep learning approach is efficient and practical and can be easily implemented for sentiment classification of COVID-19 reviews.
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