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This study aims to gain a first explorative view on what intrinsic/extrinsic attributes a generic cup for hot beverage should possess to be perceived as eco-friendly, and how some attributes of a hot beverage could influence consumers’ purchase decision and willingness to pay (WTP) for that beverage dispensed by vending machines (VMs).

A questionnaire was developed in 2021 and sent to all students of an Italian university campus. For the first goal, students were asked to assign a score to some eco-friendly intrinsic/extrinsic attributes using a five-point Likert scale. For the second aim, a choice experiment with six scenarios was developed.

Both intrinsic and extrinsic attributes play a key role in shaping students’ opinions and preferences. Results indicate that students are attracted by the idea of a cup that communicates its environmental properties through corresponding labels and information, and it is made by materials that guarantee biodegradability, recyclability or reusability.

The research represents the first academic attempt to provide a first consumers’ viewpoint on the importance of eco-friendly attributes of cups for hot beverages able to influence consumers’ perceptions and consumption choices of hot beverages dispensed by VMs.

As a result of both shifting social concerns and stricter legislation, many companies are seeking innovative and sustainable solutions for both primary and secondary packaging. The craft beer industry has made the most notable advancements with a variety of alternatives to the traditional secondary packaging (e.g. plastic rings) used to link beer cans. Yet, despite the impact packaging has on consumer purchase behavior and the environment, secondary packaging has received far less attention in the literature. This study aims to understand consumer perceptions of craft beer secondary packaging on consumers’ willingness to buy via the underlying mechanisms of perceived packaging innovation and sustainability.

A single-factor between-subjects experiment with four conditions (secondary packaging format: Pak-Tech holder, Keel Clip holder, Glue-Pack holder, Biodegradable holder) was conducted with 354 participants to test the hypothesized serial mediation model.

The results demonstrate that none of the secondary packaging styles had a direct effect on willingness to buy, meaning that the package style alone does not influence a consumer’s purchase decisions. Critically, the results also revealed indirect effects; specifically, the Keel Clip and Glue-Pack six-pack can holders were perceived as more innovative than the Pak-Tech holder, which in turn positively influenced perceptions of packaging sustainability and subsequent willingness to buy.

The authors’ results extend the research on sustainable secondary packaging and aids craft breweries along with other beverage producers in making informed decisions as the push for sustainable packaging becomes more prominent.

The importance of packaging attributes for purchase decisions has generated interest in the research and food industry. As a matter of fact, innovation in packaging is constantly searching for new solutions that generate customer experience. The aim of this study is to analyse the effect of packaging attributes (protection, convenience, portability and storage, information, sustainability, branding and engagement) on consumer purchase intention of experiential packaging especially designed to provide an extraordinary sensory or interactive communicational experience and the influence of potential moderators.

The authors used a quantitative methodology based on the partial least squares (PLS) technique to estimate the structural model proposed. A purposely developed questionnaire was administered to a non-probabilistic sample of 1,489 European consumers. The questionnaire included questions related to consumers' perception of packaging attributes and purchase intention of different experiential packages.

The results indicate that packaging attributes are related to consumer purchase intention of experiential packaging. Engagement attributes show the strongest positive influence followed by branding and economy. However, attributes such as sustainability showed a negative effect on the purchase intention of these packages. The authors’ results also show the influence of gender, family structure and residential background as moderators of the relationships.

Customers' decision-making processes are strongly influenced by product packaging. However, little is known about how new technologies and design in packaging influence consumers' responses. This research provides evidence of the influence of packaging attributes on consumer purchase intention for experiential packaging, a proliferating area of research.

This paper delves into evaluating the effectiveness of warning messages as a deterrent against excessive fat consumption. It examines how consumers perceive the fat content of food products when presented with two distinct label types: (1) a textual warning, providing succinct information about the fat content, and (2) a pictorial warning, offering a visual representation that immediately signifies the fat content.

Two quantitative studies were carried out. Study 1 employed a questionnaire to evaluate the efficacy of textual and pictorial warning messages on high- and low-fat food products. Similarly, Study 2 replicated this comparison while incorporating a neuromarketing instrument to gauge participants’ cerebral reactions.

Results indicate that pictorial warnings on high-fat foods significantly deter consumers’ purchasing intentions. Notably, these pictorial warnings stimulate the left prefrontal area of the cerebral cortex, inducing negative emotions in consumers and driving them away from high-fat food items.

While the influence of images over text in shaping consumer decisions is well understood in marketing, this study accentuates the underlying mechanism of such an impact through the elicitation of negative emotions. By understanding this emotional pathway, the paper presents fresh academic and managerial perspectives, underscoring the potency of pictorial warnings in guiding consumers towards healthier food choices.

1. Textual warnings do not seem to discourage high-fat product consumption.

2. A pictorial warning represents the fat content of an equivalent product.

3. Pictorial warnings decrease the intention to purchase a high-fat product.

4. Pictorial warnings determine an increase in negative emotions.

Textual warnings do not seem to discourage high-fat product consumption.

A pictorial warning represents the fat content of an equivalent product.

Pictorial warnings decrease the intention to purchase a high-fat product.

Pictorial warnings determine an increase in negative emotions.

This paper aims to shed new light on the contextual embeddedness of intersectional entrepreneurs, i.e. entrepreneurs situated at the intersection of multiple marginalized diversity attributes, beyond simply business strategies and decisions. Taking an emic perspective on everyday practices as intersectional entrepreneurs, it uncovers neglected dimensions of the contextuality of intersectional discriminations.

The study presents qualitative data analysis results of in-depth narrative interviews with six intersectional entrepreneurs, all LGBTIQA* entrepreneurs with further diversity dimensions. It provides an emic view of intersectional entrepreneurs, in their everyday lives and the contexts, in which they develop their businesses.

Intersectional entrepreneurs face different burdens induced by social structures along the entrepreneurial process. While access to the niche market is more difficult and they lack community support, their realization of intersectional discrimination is crucial for the development of business strategies. Simultaneously, intersectional entrepreneurs use their specific diversity attributes to develop their business, yet this proximity of their identity and the business contents has severe consequences for their mental state. Intersectional entrepreneurs adjust to the balance of opportunity and vulnerability.

As intersectional entrepreneurs are barred from conventional institutional support and also have limited access to resources from their respective social networks, there is an urgency to provide specific support for such entrepreneurs.

This paper aims to identify how cute packaging design elements can influence young adult purchases of unfamiliar products, especially the perceived old-fashioned ones.

Two studies were conducted systematically with 240 young adults. The first study manipulated product characters, fonts, colors and storytelling styles to determine the cutest packaging elements. Packaging samples containing the cutest elements from Study 1 were tested for their effect on purchase intention in Study 2, moderated by product familiarity.

Anthropomorphized product characters, curvy, handwritten-like fonts, a mixture of colors and superhero story-like product information were considered the whimsically cutest packaging elements by young adults. Whimsically cute packaging design can bridge consumer product unfamiliarity and generate higher purchase intention.

Whimsically cute packaging design could be a promising alternative for marketers promoting unfamiliar products to young adult consumers.

This study's findings complement existing literature on cute packaging design, whimsical cuteness and extrinsic cue utilization theory.

Feature extraction from 3D datasets is a current problem. Machine learning is an important tool for classification of complex 3D datasets. Machine learning classification techniques are widely used in various fields, such as text classification, pattern recognition, medical disease analysis, etc. The aim of this study is to apply the most popular classification and regression methods to determine the best classification and regression method based on the geodesics.

The feature vector is determined by the unit normal vector and the unit principal vector at each point of the 3D surface along with the point coordinates themselves. Moreover, different examples are compared according to the classification methods in terms of accuracy and the regression algorithms in terms of R-squared value.

Several surface examples are analyzed for the feature vector using classification (31 methods) and regression (23 methods) machine learning algorithms. In addition, two ensemble methods XGBoost and LightGBM are used for classification and regression. Also, the scores for each surface example are compared.

To the best of the author’s knowledge, this is the first study to analyze datasets based on geodesics using machine learning algorithms for classification and regression.

This study investigates whether the visualization of agricultural products influences consumers’ purchase intentions in the context of farmer-assisted livestreaming in China. Moreover, it explores the moderating effect of packaging functionality and the mediating effect of consumer trust.

Consumers in China from multiple social media platforms participated in this survey, which yielded 333 valid responses for analysis.

The results revealed a positive relationship between the video presentation about the agricultural production process and consumers’ purchase intention, which is mediated by consumers’ trust. Meanwhile, packaging functionality moderates the relationship between agricultural product visualization and consumers’ purchase intentions as well as the indirect effect of consumers’ trust.

This study extends the application of the stimulus-organism-response (SOR) model to the field of farmer-assisted livestreaming. By building a more detailed model, this study adds to knowledge on the influencing mechanisms of consumers’ purchase intentions in farmer-assisted livestreaming.

This study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data.

The researchers designed six locally relevant network visualization activities to support students’ data reasoning practices toward understanding aggregate patterns in data. Cultural historical activity theory (Engeström, 1999) guides the analysis to identify how network visualization activities mediate students’ emerging understanding of aggregate data sets.

Pre/posttest findings indicate that this implementation positively impacted students’ understanding of network visualization concepts, as they were able to identify and interpret key relationships from novel networks. Interaction analysis (Jordan and Henderson, 1995) of video data revealed nuances of how activities mediated students’ improved ability to interpret network data. Some challenges noted in other studies, such as students’ tendency to focus on familiar concepts, are also noted as teachers supported conversations to help students move beyond them.

To the best of the authors’ knowledge, this is the first study the authors are aware of that supported elementary students in exploring data literacy through network visualization. The authors discuss how network visualizations and locally/personally meaningful data provide opportunities for learning data literacy concepts across the curriculum.

Surface curvature is needed to analyze the range data of real objects and is widely applied in object recognition and segmentation, robotics, and computer vision. Therefore, it is not easy to estimate the curvature of the scanned data. In recent years, machine learning classification methods have gained importance in various fields such as finance, health, engineering, etc. The purpose of this study is to classify surface points based on principal curvatures to find the best method for determining surface point types.

A feature selection method is presented to find the best feature vector that achieves the highest accuracy. For this reason, ten different feature selections are used and six sample datasets of different sizes are classified using these feature vectors.

The author examined the surface examples based on the feature vector using the machine learning classification methods. Also, the author compared the results for each experiment.

To the best of the author's knowledge, this is the first study to examine surface points according to principal curvatures using machine learning classification methods.