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The increasing accumulation of synthetic plastic waste in oceans and landfills, along with the depletion of non-renewable fossil-based resources, has sparked environmental concerns and prompted the search for environmentally friendly alternatives. Biodegradable plastics derived from lignocellulosic materials are emerging as substitutes for synthetic plastics, offering significant potential to reduce landfill stress and minimise environmental impacts. This study highlights a sustainable and cost-effective solution by utilising agricultural residues and invasive plant materials as carbon substrates for the production of biopolymers, particularly polyhydroxybutyrate (PHB), through microbiological processes. Locally sourced residual materials were preferred to reduce transportation costs and ensure accessibility. The selection of suitable residue streams was based on various criteria, including strength properties, cellulose content, low ash and lignin content, affordability, non-toxicity, biocompatibility, shelf-life, mechanical and physical properties, short maturation period, antibacterial properties and compatibility with global food security. Life cycle assessments confirm that PHB dramatically lowers CO₂ emissions compared to traditional plastics, while the growing use of lignocellulosic biomass in biopolymeric applications offers renewable and readily available resources. Governments worldwide are increasingly inclined to develop comprehensive bioeconomy policies and specialised bioplastics initiatives, driven by customer acceptability and the rising demand for environmentally friendly solutions. The implications of climate change, price volatility in fossil materials, and the imperative to reduce dependence on fossil resources further contribute to the desirability of biopolymers. The study involves fermentation, turbidity measurements, extraction and purification of PHB, and the manufacturing and testing of composite biopolymers using various physical, mechanical and chemical tests.

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

This research aims to examine the challenges of recruiting participatory action research (PAR) in managing innovation projects. An enhanced methodology based on PAR was developed to mitigate the challenges related to recruiting PAR in managing innovation projects. The proposed methodology was evaluated by comparing it to established methodologies/frameworks such as Scrum, Design Thinking (DT) and The Lean Startup (TLS). The evaluation aimed to determine the advantages and limitations of the proposed methodology in managing innovation projects.

The proposed enhanced methodology consists of eight steps, ranging from developing an understanding of the industry and business structure to learning and knowledge management. In addition, the enhanced methodology uses other techniques, such as Force field analysis and 12 boundary questions.

The research findings indicate that using the proposed methodology can improve the formalization of collaboration in PAR, enabling the organization to respond better to market changes. It helps define the project scope more clearly, encouraging innovation, addressing communication barriers and considering different worldviews and practical issues. Based on the findings, the proposed enhanced methodology could complement other methodologies/frameworks such as Scrum, DT and TLS.

The current research adds to the existing literature by identifying the challenges of recruiting PAR in managing innovation projects. A deductive reasoning process was utilized because there is no comprehensive research concerning the challenges of recruiting PAR in managing innovation projects. On the other hand, the PAR 4-phase cycle has been reviewed and enhanced to manage innovation projects.

The proposed methodology was used in a new product development project. The case study was done on one of the payment service provider companies that design, develop and deploy a digital product for marketing, installation, repair and maintenance of electronic funds transfer at point of sale devices.

No research has yet sought to identify the challenges of using PAR in innovation project management (IPM). Identifying the challenges associated with applying PAR in the IPM and providing an enhanced methodology to mitigate the challenges could fill a gap in IPM studies.

This study aims to provide a timely “first look” at the impact of COVID-19 lockdown restrictions on the financial distress of nonprofits.

This study uses Internal Revenue Service Form 990 returns, US census information, and Oxford COVID-19 workplace restriction data and utilizes logistic regression to analyze results.

Nonprofits with greater COVID-19 lockdown restrictions are more likely to experience financial distress, whether measured by a 30% reduction in total, program, management and general, or fundraising expenses. This paper also examines results by subsector using National Taxonomy of Exempt Entities data and finds that the Human Services and Public and Society subsectors drive the full sample results when the authors use total, program, or managerial and general expenses in the measure of financial distress, and the Education and Environment and Animals subsectors drive the results when using fundraising expenses in the measure of financial distress.

Broadly speaking, this paper contributes to the limited research stream examining the impact of crises on nonprofits. More specifically, this study is among the earliest to rely on quantitative data to investigate such effects.

This chapter presents a comparative study of the trends and patterns of communication and tourism research in Brazil and the United States over the last 20 years. Through a bibliometric analysis of the CAPES and EBSCO databases, the study identifies the main theoretical and methodological references, classifies the fundamental themes in the area, and describes the role of communication for tourism. The results indicate the predominance in North American scientific literature of research related to the image and the brand of the tourist destinations, as well as the measurement and the evaluation of the communicative strategies. On the other hand, Brazilian research presents a greater diversity of approaches: destination image studies, tourism consumption, tourist narrative analysis, identities, social networks, community-based tourism, sports, and ecological tourism, with an explicit recognition of the dangers of sexual objectification and dehumanization within tourism. The survey showed that the scientific community has a strong interest in this area, signaling a search for knowledge to deepen the conceptual understanding of the subject. Thus, this chapter provides insights regarding the opportunities and directions for the next decades of research in this field of study.

In keeping with recent body image social trends, consumer demand for the adoption of plus-size models is increasing, although the use of thin models remains prevalent. The current study explores how consumers process information about fashion products displayed on different sizes of models in advertisements, focusing on model and consumer body sizes and both genders. As an underlying mechanism explaining how the relationship between model and consumer body sizes shapes consumer purchase intention, this study explores the role of guilt, shame and mental imagery.

The current study uses a text analytics technique to identify female consumers' general opinions of thin models in advertising. Employing a 3 (consumer body size: normal, overweight, obese) × 2 (model body size: thin, plus-size) × 2 (gender: male, female) between-subjects online experiment (n = 718), the main study comparatively analyzes the influences of plus-size and thin models on consumer responses.

The results reveal that, despite body positivity movements, thin models still generate negative emotions among female consumers. For obese female consumers, advertisements featuring plus-size models produce fewer negative emotions but not more mental imagery than advertisements featuring thin models. Conversely, for obese male consumers, advertisements featuring plus-size models generate more mental imagery but not more negative emotions than advertisements featuring thin models. The results also reveal that the relationship between consumer body size and guilt is moderated by perceived model size, which is also moderated by gender in generating mental imagery. While guilt plays a mediating role in enhancing mental imagery, resulting in purchase intention, shame does not take on this role.

This study is the first to present an integrated model that elucidates how consumers with varying body sizes respond to different sizes of models in advertising and how these responses impact purchase intentions.

Our findings only apply to contexts where consumers purchase fashion clothing in response to advertisements featuring thin versus plus-size models.

Exposing normal-size consumers to plus-size models generates less mental imagery, and thus, practitioners should seek to match the body sizes of the models featured in advertising to the body sizes of their target audience or ad campaigns that include both plus-size and thin models may help improve message persuasiveness in fashion advertising. Moreover, guilt-appeal advertising campaigns using thin models would appeal more to thin consumers of both genders than shame-appeal advertising.

Pursuit of meaning is at the heart of much of organizational life. It has implications for how different organizational stakeholders associate value to various organizational initiatives. Research on meaning has generally shown that effort increases meaning and favorable valuation of and willingness to pay for economic activities by organizational stakeholders. The authors build on and advance this research by offering theory and experimental evidence showing that effort, particularly at high levels, results in enhanced meaning and favorable valuation when effort does not threaten the focal stakeholders' resources through expectation disconfirmation.

Three experimental studies are designed and conducted in this research. In one study, the authors replicate prior research findings that establish labor generally increases meaning and favorable valuation. In the two subsequent studies, the authors test the proposed hypothesis in this research and check for robustness of the empirical analysis.

The authors find that any internalized threat to the focal stakeholder's resources coupled with a high exertion of effort decreases, rather than increases, meaning and favorable valuation of and willingness to pay for economic activities.

The theory and empirical evidence in this research advance the understanding of how organizational stakeholders may associate effort-induced meaning with various economic activities in counter-intuitive ways. The findings also highlight the importance of recognizing and shaping the expectations of organizational stakeholders in influencing willingness to pay in organizational settings.

The growing focus on socially responsible supply chain management (SRSCM) has made it crucial to extend corporate social responsibility (CSR) to upstream suppliers. Drawing on resource dependence theory, this study aims to examine how supplier dependence upon socially responsible buyers impacts suppliers' CSR performance and how this relationship is moderated by network prominence and demand uncertainty.

The proposed hypotheses are tested using regression analysis with Heckman's two-stage model and a dyadic supply chain dataset constructed based on publicly traded Chinese firms between 2008 and 2016. This time window is selected due to a one-year lag of the dependent variable and the change in evaluation methods of the database providing CSR performance in 2018.

The empirical results indicate that supplier dependence upon socially responsible buyers is positively associated with suppliers' CSR performance. However, this positive relationship is attenuated when suppliers occupy a prominent position in the network or when they face high demand uncertainty.

This study extends knowledge about the role of relationship dependence in implementing SRSCM by highlighting its positive impact on suppliers' CSR. Thus, this study contributes to the buyer–supplier relationship literature and the power and relationship dependence literature. This study further advances the understanding of the factors that influence suppliers' behavior by exploring the moderating roles of network prominence and demand uncertainty. The results have several practical implications for managers and policymakers.

The purpose of this study is to evaluate the efficiency of a Brazilian steelmaking company’s reheating process of the hot rolling mill.

The research method is a quantitative modeling. The main research techniques are data envelopment analysis, TOBIT regression and simulation supported by artificial neural networks. The model’s input and output variables consist of the average billet weight, number of billets processed in a batch, gas consumption, thermal efficiency, backlog and production yield within a specific period. The analysis spans 20 months.

The key findings include an average current efficiency of 81%, identification of influential variables (average billet weight, billet count and gas consumption) and simulated analysis. Among the simulated scenarios, the most promising achieved an average efficiency of 95% through increased equipment availability and billet size.

Additional favorable simulated scenarios entail the utilization of higher pre-reheating temperatures for cold billets, representing a large amount of savings in gas consumption and a reduction in CO2 emissions.

This study’s primary innovation lies in providing steelmaking practitioners with a systematic approach to evaluating and enhancing the efficiency of reheating processes.