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Although photographs in sustainability reports are useful in conveying complex messages, they may also be used to manipulate the presentation of disclosures to exploit the limited cognitive processing capacity of humans. Therefore, this paper aims to examine the features of photographs aimed at capturing individuals’ attention through visual structures and evoking specific emotions through carefully chosen content. Furthermore, it examines whether such framing practice is explained by incentives for legitimizing behaviours and influencing reputation.

The authors conduct a content analysis of photographs in 154 sustainability reports published by US companies. The authors captured the nature of photographs, the context in which they are being used, their themes and emotional content and layout and interaction features to understand how photographs are used for attribute framing to influence information processing. Furthermore, the authors statistically examine the framing practice between companies with different characteristics to identify any patterns for the impression management use of photographs in sustainability reports.

Photographs are often large with a horizontal orientation to capture attention and show content viewed at eye level and in either medium or close-up shots to engage viewers. Furthermore, photographs are emotionally loaded with different themes such as depictions of people, technology and nature. These themes are used to predominately evoke positive emotions of awe, nurturance, pride, amusement and attachment. This practice is often used by companies in environmentally sensitive areas that have close consumer relationships or are covered controversially in the media.

The authors reveal reporting practices and identify photographic features that attract attention and convey emotions that go beyond aesthetic qualities. This is important because emotions conveyed through photographs can be potentially misleading and influence judgements subconsciously.

Introduction: Lending is an age-old concept, and Peer-to-Peer (P2P) lending is not new. The reduction in the issuing of loans by banks has made people switch from traditional to online mode. The introduction of the online P2P lending industry is in its nascent stage of growth. As this industry is relatively new, understanding user experience, sentiments, and emotions would be helpful for the industry to innovate as per customer requirements.

Purpose: To explore the patterns in the sentiments expressed by users of ‘Cashkumar’ based on Google reviews.

Methodology: Sentiments have been analysed using user experience in risk, cost, ease of use, and loan processing time. Python application was used for sentiment analysis of Google reviews.

Findings: The sentiment analysis results showed that the average sentiment score was 0.7144, which indicates that the user sentiment towards ‘Cashkumar’ is positive. The reviews reflect that the users, especially borrowers were satisfied with the platform’s services and happy with loan processing time. The other factors – ease of use, cost, and risk – were not given much importance by users. Both lenders and borrowers faced a few issues, but the results of the lender’s sentiment analysis could not be generalised due to a smaller number of posted reviews.

Need: The previous suggestion assists with administrative methodology. The contribution explores customer understandings in different industry and transaction texts. They include online education, video marketing, and entertainment analytics. The communication needs to be detailed to improve the system.

Purpose: The suggestion aims to improve the previous contribution by enhancing the user experience. The study increases the usage of video content. The recommendation brings better business to the video host.

Methodology: The work includes the machine learning algorithm to understand the user and improve the client’s experience. The recommendation uses the Apriori algorithm to map various attributes of the trainer and learners. The suggested work has three features. It focusses on video possessions, educator feelings, physical characteristics, and visible aesthetic characteristics. The study considers 1,200 different samples.

Findings: The work simulates using python. It improves efficiency by 29.5% compared to previous work.

Practical Implications: Machine learning has pitched in to understand diverse customers’ behaviour. Various features affecting the behaviour are collected and analysed by the system. The study intends to find an appropriate mapping between the attributes of the user and educator.

The implications of metallic biomaterials involve stress shielding, bone osteoporosis, release of toxic ions, poor wear and corrosion resistance and patient discomfort due to the need of second operation. This study aims to use additive manufacturing (AM) process for fabrication of biodegradable orthopedic small locking bone plates to overcome complications related to metallic biomaterials.

Fused deposition modeling technique has been used for fabrication of bone plates. The effect of varying printing parameters such as infill density, layer height, wall thickness and print speed has been studied on tensile and flexural properties of bone plates using response surface methodology-based design of experiments.

The maximum tensile and flexural strengths are mainly dependent on printing parameters used during the fabrication of bone plates. Tensile and flexural strengths increase with increase in infill density and wall thickness and decrease with increase in layer height and wall thickness.

The present work is focused on bone plates. In addition, different AM techniques can be used for fabrication of other biomedical implants.

Studies on application of AM techniques on distal ulna small locking bone plates have been hardly reported. This work involves optimization of printing parameters for development of distal ulna-based bone plate with high mechanical strength. Characterization of microscopic fractures has also been performed for understanding the fracture behavior of bone plates.

This paper aims to identify, examine, and present an empirical research design of behavioral finance of potential investors during Covid-19.

A well-structured questionnaire was designed; a survey was conducted among potential investors using convenience sampling, and 200 valid responses were collected. The research work uses multiple regression and discriminant function analysis to evaluate the influence of cognitive factors on the financial decision-making of investors.

Recency and familiarity bias are proven to have the highest significant impact on the financial decisions of investors followed by confirmation bias. Overconfidence bias had a negligible effect on the decision-making process of the respondents and found insignificant.

Covid-19 is a temporary phase that may lead to changes in financial behavior and investors’ decisions in the near future.

The paper will help academicians, scholars, analysts, practitioners, policymakers and firms dealing with capital markets to execute their job responsibilities with respect to the cognitive bias in terms of taking financial decisions.

The present investigation attempts to fill the gap in the literature on the intended topic because it is evident from literature on the chosen subject that no study has been undertaken to evaluate the impact of cognitive biases on financial behavior of investors during Covid-19.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

The purpose of this research is present an experimental and numerical study of the mechanical properties of the acrylonitrile butadiene styrene (ABS) in the additive manufacturing (AM) by fused filament fabrication (FFF). The characterization and mechanical models obtained are used to predict the elastic behavior of a prosthetic foot and the failure of a prosthetic knee manufactured with FFF.

Tension tests were carried out and the elastic modulus, yield stress and tensile strength were evaluated for different material directions. The material elastic constants were determined and the influence of infill density in the mechanical strength was evaluated. Yield surfaces and failure criteria were generated from the tests. Failures over prosthetic elements in tridimensional stresses were analyzed; the cases were evaluated via finite element method.

The experimental results show that the material is transversely isotropic. The elasticity modulus, yield stress and ultimate tensile strength vary linearly with the infill density. The stresses and the failure criteria were computed and compared with the experimental tests with good agreement.

This research can be applied to predict failures and improve reliability in FFF or fused deposition modeling (FDM) products for applications in high-performance industries such as aerospace, automotive and medical.

This research aims to promote its widespread adoption in the industrial and medical sectors by increasing reliability in products manufactured with AM based on the failure criterion.

Most of the models studied apply to plane stress situations and standardized specimens of printed material. However, the models applied in this study can be used for functional parts and three-dimensional stress, with accuracy in the range of that obtained by other researchers. The researchers also proposed a method for the mechanical study of fragile materials fabricated by processes of FFF and FDM.

Drawing on goal-setting theory and team effectiveness theory, the study aims to examine the impact of agile taskwork and agile teamwork on team performance. In addition, it investigates the mediating effect of project commitment on the impact of agile taskwork and agile teamwork on team performance. Furthermore, the study also tests the moderating role of career level on the impact of agile taskwork and agile teamwork on team performance.

Survey data were collected from 563 employees working in 290 information technology (IT) companies in India using a self-reporting structured questionnaire. Partial least squares path modeling was used to test the hypothesized model, and the Process macro was used to test the moderating effect.

The results show that agile taskwork and agile teamwork positively affect team performance and project commitment, and project commitment positively impacts team performance. Furthermore, project commitment fully mediates the relationship between agile taskwork and team performance and partially mediates the relationship between agile teamwork and team performance. Furthermore, the career level negatively moderates the impact of agile taskwork and agile teamwork on team performance.

The study shows the importance of agile work practices and project commitment to enhance team performance. Thus, the study provides managers with two strategies to improve their team performance.

There is a scarcity of research examining the distinct effects of agile taskwork and agile teamwork on team performance and the mediating role of project commitment in these relationships. Furthermore, as per the empirical evidence, no previous research has empirically examined the moderating role of career level in the agile taskwork-team performance and agile teamwork-team performance relationships.

As corporate sustainability is systemic, it cannot be achieved without effective involvement of suppliers. This study aims to examine the drivers of supplier companies’ resource allocation to a sustainability issue that affects customer companies and society at large.

Supplier companies’ resource allocation for a sustainability issue is explained from variables at the levels of the institutional, supply chain and internal environments of a supplier company. The framework is tested with a moderated regression model on 102 supplier companies in animal-based supply chains, focussing on their resource allocation for farm animal welfare.

The findings show that supply chain factors have the strongest influence on suppliers’ resource allocation, including a strong effect of investment specificity and a U-shaped effect of chain integration. Also, significant effects from institutional variables, namely, the pressure on consumer companies, and an inverted U-shaped effect of sustainability competition are found. The innovativeness, referring to the internal environment of supplier companies, appears as another important factor for the allocation of resources to animal welfare, as a sustainability issue.

The results have implications for consumer market companies to deal with sustainability issues that require involvement of their suppliers, for supplier companies to increase their competitive positions and strengthen their relationships within the supply chain, and for policymakers seeking solutions for sustainability issues in the market domain.

While existing literature focusses mostly on the corporate sustainability of highly visible and large consumer companies, to the best of the authors’ knowledge, this study is the first to examine the drivers of supplier companies’ resource allocation for a sustainability issue, namely, animal welfare. It provides insights on what drives supplier companies, usually operating outside the spotlight, to become part of a sustainability transition.