Australian Institute of Business

This research set out to find out how well projects are complying with the requirements of the Environmental Impact Assessment Guidelines in Zimbabwe. Data was collected from EIA reports completed between 2007 and 2012. Questionnaires and interviews were used to collect the experiences of practitioners in the EIA sector. The results show that EIA reports contain below 65% of required information for decision making. Critical sections of the EIA report are the most deficient. Compliance varies significantly between consultants, stage of EIA process and size of project. Recommendations are: need to review and expand the existing guidelines and promote their use, building objectivity into the EIA report review process, upholding professional standards of practice for the consultants, improvement of compliance monitoring and enforcement, the use of economic incentives and disincentives other than enforcement to promote compliance, increased awareness raising of EIA in the business sector, and increased political will and transparency.

Product refurbishment enhances waste minimization and environmental sustainability. However, the sale of these products relies on consumer attitudes, influenced by various factors. This study adopts a novel mixed-methods approach, utilizing structural and network analysis based on over 60,000 comments and tweets from X.com (formerly Twitter), complemented by insights from expert interviews, to analyze factors affecting consumers viewpoints when making decisions about purchasing refurbished electronics. The research identifies and prioritizes 17 key influencing factors, highlighting the interplay between large-scale consumer sentiment and expert opinions. A customer-centric purchase decision model is presented, showing that consumer priorities when purchasing refurbished products typically revolve around retailer reputation, brand, pricing, warranty, and product quality. These findings offer valuable guidance for producers on how to target higher-priority consumer concerns, ultimately helping to expand their market and boost purchasing intention.

This study adopts a multidimensional approach to examining sustainability reporting practices among notebook manufacturers, integrating traditional corporate sustainability reports with social media data from platforms like Twitter, Facebook, and Reddit. Using advanced machine learning techniques, including multi-label classification and BERT-based sentiment analysis, the research highlights the key environmental, social, and economic dimensions that consumers prioritize. The findings reveal a significant misalignment between consumer concerns, reflected in social media discourse, and the focus of corporate sustainability reports. While companies emphasize environmental impacts and supply chain management, consumers prioritize product reliability, community obligations, and educational initiatives. This gap indicates a need for companies to realign their sustainability strategies to better meet consumer expectations, emphasizing trust-building, community engagement, and educational efforts. Despite these differences, the study also identifies shared concerns between consumers and producers, such as environmental impacts, supply chain sustainability, and transparency in sustainability claims. Based on these insights, the study recommends fostering transparent communication, engaging in inclusive dialogue, and committing to accountable actions across all sustainability dimensions. By aligning corporate reporting with consumer expectations, this research provides guidance to help companies advance towards a circular economy and enhance Extended Producer Responsibility (EPR) in the electronics industry, ultimately benefiting both companies and consumers in the pursuit of a more sustainable future.

This study examines the relationship between food safety risk perception (FSRP) and social similarity, utilizing eye-tracking to uncover how consumers process information on food labels. Central to our inquiry is that perceived similarity influences how individuals assess and react to food safety risks. Our research posits that individuals who see a high degree of similarity with others in their social circles are more attentive to food safety risks. We conducted two food label experiments, one focusing on safety labels and the other on nutritional information. Findings indicate that heightened perceptions of social similarity (prototype perception and social distance) enhance the impact of food labels (safety labels and nutritional information) on food safety risk assessments (decision-making process and product choice), manifesting in increased visual attention to these labels. This revelation underscores the significance of social factors in shaping food safety behaviors and offers a new perspective for designing more effective food safety communication. Our work contributes to the academic discourse on food safety and social psychology and provides practical insights for public health messaging strategies.

This study investigates the impact of reward timing (immediate versus delayed rewards) on conative loyalty (deliberate, intention-based commitment to repeatedly choose a specific brand, even in the face of competing alternatives) within tourism and hospitality loyalty programs. Employing eye-tracking technology, we examine how different levels of fairness perception and visual attention influence conative loyalty, contingent on reward timing. Our experiment utilizes simulated loyalty program advertisements from travel agencies and restaurants to assess these dynamics. Findings reveal that immediate rewards significantly enhance conative loyalty compared to delayed rewards, especially when coupled with heightened fairness perceptions and visual engagement. The elasticity of conative loyalty escalates with heightened perceived fairness and increased visual attention—a trend more pronounced with immediate rewards. Conversely, the influence of visual attention on conative loyalty is notably absent in delayed reward scenarios. Therefore, the study underscores the critical influence of immediate gratification in loyalty programs, illustrating that timely rewards can be a decisive factor in solidifying conative loyalty and ensuring customer retention in the competitive tourism and hospitality sector. Keywords: conative loyalty, loyalty programs, reward timing, fairness perceptions, visual attention.

This research proposed a multi-industry framework that aims to clarify reverse logistics decision-making through a social media analytics approach analyzing the electronics industry in developing compared to developed countries in separated case studies. By leveraging deep learning on social media data, this study intends to elucidate ideal product return policies and strategies aligned with sustainability, circular economic principles, and sustainable development goals. Furthermore, this work outlines the potential of social media analytics to align consumer expectations with supply chain decisions across geographic contexts and identify the missing potential stream of material in supply chains. Overall, this study utilizes social media data mining and sentiment analysis to shed light on optimal reverse logistics decision-making for electronics firms in diverse national settings pertinent to circular economy ideals. The juxtaposition of developing and developed nations provides an enhanced understanding of how optimal return policies may differ given geographic and economic contexts. In summary, this research elucidates data-driven reverse logistics insights via social media analytics to inform electronics sector circular economy strategies tailored to national development levels.

Employee performance is affected by both individual-and team-level factors. The current work investigates the impact of an individual's metacognitive ability on their performance at the workplace while accounting for the impact of team characteristics on employee performance. The responses for the study were collected from 180 work teams consisting of 1,216 employees. The respondent teams were classified as one of the 36 distinct types of teams conceptualized in the study. Multi-level modelling was used to explore employee performance, both in conjunction with and independent of team characteristics. The analysis was done on only accurately calibrated responses (n = 852). The results demonstrated a 33.8% variance in employee performance that can be explained by metacognitive ability alone. The findings also suggest that metacognitive ability positively impacts the performance of employees across a wide variety of work teams, such that most team characteristics fail to significantly impact employee performance once the impact of metacognitive ability is accounted for. Though team tasks and team size continue to impact employee performance, their incremental contribution to explaining the variance in employee performance becomes quite small. The results strongly support the influence of metacognitive ability on employee performance. The implications of the study are discussed.

Big data analytics have impacted nearly every service industry in the last decade. Furthermore, using artificial intelligence in big data analytics has introduced a new trend, resulting in different performance types, e.g., sales, marketing, innovation, organizational, financial, and operational. A systematic review of the empirical results from publications addressing big data analytics in the services industry becomes necessary to understand these performances better. Based on this rationale, this study conducted a meta-analysis to identify the relevant dimensions of big data analytics and evaluate artificial intelligence as a potential moderator of its effects on service performance. The results demonstrate that environmental dynamism, resources and capabilities, and competitive pressure drive big data analytics adoption. Environmental dynamism, followed by resources and capabilities, has greater effects on adopting big data analytics. The findings suggest that adopting big data analytics powered by artificial intelligence enhances service performance more than adopting big data analytics without using artificial intelligence.

The supply chain forms the backbone of the modern consumer economy, weaving an intricate network of stakeholders across geographical and socioeconomic divides. While new technologies have enhanced supply chain management, the market dynamism and network complexities continue to challenge decision-makers. This study employs social network analysis and text mining to unravel technological patterns within the patent landscape of supply chain management. The analysis draws on a dataset of over 32,000 supply chain patents from Lens.org spanning 2000–2022. Network analysis reveals cooperation patterns and key players, while text mining and clustering identify five technology clusters: secure access control, manufacturing, logistics, data management, and RFID. Technology life cycle analysis indicates that secure access control, data management, and RFID have reached maturity, while logistics is still growing and manufacturing faces saturation. The findings highlight that despite maturity, these technologies warrant continued investment to resolve persistent challenges. The technology trends and maturity insights uncovered can help enterprises make informed strategic decisions by aligning R&D initiatives with technology lifecycles. This pioneering study bridges innovation research and technology management, offering a nuanced understanding of supply chain technologies. The framework presented can be extended to analyze other domains, opening avenues for further research. Overall, this study decodes the patent landscape to decode the future.

This study explores the evolution of customer engagement in the digital era for businesses, a crucial concept in today’s interconnected marketplace facilitated by a variety of digital platforms. The study identifies a gap in the comprehensive understanding and conceptualisation of digital customer engagement across various social media platforms and mediums. By using a bibliometric analysis approach, this study reviews 547 articles published between 2010 and 2021, offering a holistic view of the existing research landscape. The analysis uncovers four distinct bibliometric clusters representing unique facets of customer engagement: brand engagement, behavioural insights, loyalty dynamics, and relationship dynamics. These clusters provide insights into the multifaceted nature of customer relationships and engagements facilitated by digital platforms. The study concludes by proposing seven future research directions, aiming to guide scholars in exploring uncharted territories in digital customer engagement and its implications for businesses in the digital age. Keywords: Customer engagement; digital era; bibliometric analysis; bibliographic coupling; research directions.

Artificial intelligence (AI) tools, notably ChatGPT, are increasingly recognised for their transformative potential in higher education. This study employs a detailed case study approach complemented by a survey, delving into ChatGPT's impact on pedagogical practices, student engagement, and academic performance. It involved 74 undergraduate and postgraduate students enrolled in data analytics courses in Australia. The quantitative analysis highlights ChatGPT's role in providing personalised and on-demand support, which is highly valued among users for its flexibility and responsiveness , meeting a critical demand in educational settings. Notably, the study identifies a medium effect size (2 = 0.173) in perceived benefits, indicating that ChatGPT accounts for approximately 17.3% of the variance in improved academic outcomes. However, challenges such as ChatGPT's limited understanding of complex queries and the lack of human interactions are primary concerns, with a medium effect size (2 = 0.289) suggesting significant areas for improvement. Furthermore, statistical analyses reveal a clear relationship between the frequency of ChatGPT usage and the perception of its benefits, underscoring the transformative potential for users who have integrated it into their academic practices. Despite these challenges, the differential impact on users versus non-users highlights the potential for ChatGPT to foster more engaging and effective educational practices. The findings advocate for targeted strategies to epitomise ChatGPT's integration into educational settings, emphasising the need for ongoing research and the development of comprehensive guidelines to navigate its complexities and maximise its educational benefits.

Supply chain resilience is essential for companies to survive in today's competitive market, as they face environmental and unforeseeable challenges in their supply chain. This paper aims to model and manage the factors and activities that influence supply chain resilience and how they relate to each other. This will help us devise plans for enhancing the resilience of a supply chain. By taking into account the factors and activities and their interrelationships, organizations can use their limited resources more efficiently to improve their supply chain resilience. We use a management matrix to rank the factors based on how they affect and contribute to the supply chain resilience. We conduct an empirical study in a pharmaceutical company to demonstrate the proposed management approach and provide improvement scenarios based on the ranking of the factors. The results show that the most important factors are “the cooperation and trust between supply chain members”, “Visibility & Agility”, and “Leadership Support and Commitment”. The ranking of the factors may vary in different companies. Therefore, other companies can apply the method described in this paper and perform different improvement scenarios according to the ranking of the factors to effectively allocate their limited management efforts.

This study aims to investigate the opportunities and difficulties presented by the COVID-19 pandemic to supply chain sustainability. This study identifies the major constructs that are crucial to the supply chain sustainability, which have not gotten sufficient attention in the literature. Therefore, we conducted a three-phase e-Delphi study using qualitative and quantitative techniques. Responses were gathered from 15 worldwide supply chain and sustainability experts, who initially identified 43 obstacles and 24 opportunities. Following the completion of the three phases of the e-Delphi process, a total of 13 vital constructs related to supply chain sustainability were identified. The current endeavor aims to close knowledge gaps related to the difficulties and opportunities produced by the Coronavirus outbreak throughout the whole supply chain, particularly when a comprehensive assessment is necessary. The results of this study will help inspire and motivate future studies in the same area.

So far, most ethical consumerism research has been contained within Western countries, thus limiting our understanding of the concept in emerging markets. Given the call for extending empirical-based knowledge for a better understanding of peculiarities, dynamics and country-level variations (i.e. social, cultural) in the context of ethical consumerism in emerging markets, this research cross-examines the interactive nature of individual- and country-level predictors of ethical consumerism in emerging and developed markets, employing a multilevel approach. At the individual level, we posit that ethical consumerism is motivated by social and cultural capital. In contrast, at the contextual level, we choose country-level affluence as an influential factor that might impact the relationship between socio-cultural capital and ethical consumerism. The study uses the International Social Survey Programme’s (ISSP) 2014 citizenship module data set (including 34 countries) for investigating individual-level predictors (of social and cultural capital). The GDP per capita data from the International Monetary Fund’s (IMF) Economic Outlook database was used to examine the cross-level interactions between individual-level predictors and country-level affluence. The findings suggest that social and cultural capitals positively influence ethical consumerism in emerging and developed markets. Further, country-level affluence moderates the relationship between socio-cultural capital and ethical consumerism for both markets. However, cultural capital proved to be a stronger predictor of ethical consumerism as country-level affluence increases. The research findings highlight meaningful cross-country-level interactions that help further understand the basis of ethical consumerism from a global perspective.

Tourist engagement (TE) has gained prominence in academia and industry. While previous studies have focused on exploring TE in tourism and hospitality, no consolidated empirical study has been conducted. We conducted a comprehensive meta-analysis using data from 73 independent studies retrieved from 72 papers, with a total sample size of 41,757. Our analysis, using meta-analytic structural equation modelling, tested a conceptual framework and found that tourist experience and TE individually mediate tourists’ satisfaction, emotion, behavioral intention, and loyalty. Additionally, cultural (e.g., power distance, individualism, uncertainty avoidance, indulgence) and economic (e.g., human development index) moderators influence TE. Our findings also suggest that popular global destinations enhance the relationship between tourist experience, engagement, and behavioral intention.