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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.

The purpose of this research is to demonstrate the effectiveness of pictorial warning labels that leverage the risk of obesity as a deterrent against alcohol abuse. It evaluates the impact of three different kinds of warning labels that can potentially discourage alcoholic drinking: (1) a claim, in text format, that cautions consumers about the product (i.e. a responsibility warning statement); (2) a textual warning label, text-format information on the content of the product or the consequences of excessive consumption (i.e. a synthetic nutritional table); (3) a pictorial warning label, an image depicting a food product with a caloric content equivalent to that of an alcoholic beverage.

In Study 1, a 2 × 2 × 2 factorial design is used to evaluate the intention to buy different alcoholic cocktails. The stimuli comprised two cocktails that are similar in alcoholic volume, but different in their caloric content. The images of the products were presented across eight warning label conditions and shown to 480 randomly selected Italian respondents who quantified their intention to buy the product. In Study 2, a different sample of 34 Italian respondents was solicited with the same stimuli considered in Study 1, and neuropsychological measurements through Electroencephalography (EEG) were registered. A post hoc least significance difference (LSD) test is used to analyse data.

The results show that only the presence of an image representing an alcoholic beverage's caloric content causes a significant reduction in consumers' purchase intentions. This effect is due to the increase in negative emotions caused by pictorial warning labels.

The findings provide interesting insights on pictorial warning labels, which can influence the intention to purchase alcoholic beverages. They confirmed that the use of images in the warning labels has a greater impact than text, and that the risk of obesity is an effective deterrent in encouraging consumers to make healthier choices.

The purpose of this paper is to find out whether the plain packaging format in cigarette labeling is worth adopting or not.

A lab experiment with a 2 (existing vs plain packaging format) × 2 (familiar vs unfamiliar brand) factorial design was conducted with Chinese subjects in Macau.

The plain packaging format in cigarette labeling reduces both smoking intent and brand likability in familiar brand condition but not unfamiliar brand condition.

When many governments are currently deliberating about whether to follow the plain packaging initiative, this study constitutes a timely investigation of the effects of it on smoking intent and brand likability among Chinese young non-smokers. The positive effect of the plain packaging in familiar brand condition provides the justification of adopting it by the governments.

Studies of plain packaging have not been taking brand familiarity into consideration (the only exceptional study used the top three familiar brands and thus failed to examine the familiarity effect) but the study focussed on it. In the data analysis, if brand familiarity is not considered, wrong conclusion will be drawn. Therefore, by having brand familiarity as moderator, the authors are able to correctly conclude that plain packaging format is worth adopting.

Cooperation of a pilot with an automated aircraft control and monitoring systems is a problem which should be solved designing the whole system. The method of design, which creates an assistant of a pilot, is the purpose of this study.

An analysis of human factors shows demands for working environment. An integration method for various technological systems and algorithms is searched.

It is possible to make the whole system to become a pilot assistant, which has ability to exchange information with pilot by a dialogue. Structural flexibility is obtained in multi-agent system structure.

Proposed approach is a solution of how to integrate increasing amount of aircraft systems. It is expected that new form of cooperation fits to human features. Proposed methodology solves problem of simultaneous control by two controllers and cooperative making decisions.

Dialogue between human and the system proposed in this solution will change perception of machines.

New abilities of machines and proposition of their realisation are presented. Presented solution of simultaneous control and decision-making during aircraft control is a novel approach to human–machine cooperation.

The purpose of this paper is to review the use of adaptive systems in education. It is intended to be a useful introduction for the non-specialist reader.

A distinction is made between intelligent tutoring systems (ITSs) and adaptive hypermedia systems (AHSs). The two kinds of system are defined, compared and contrasted. Examples of the implementation of the two kinds of system are included.

Similarities and differences between the two kinds of system are highlighted. A conceptual unification is proposed based on the architecture of Course Assembly System and Tutorial Environment, a seminal prototype learning environment developed by Pask and Scott in the 1970s as an application of Pask’s conversation theory.

The architecture shows how the key aspects of ITSs and AHSs can be combined to complement each other. It is intended to be an original contribution that is of particular interest for the specialist reader.

The purpose of this study is to identify which social and psychological factors better determine intentions to quit smoking to inform public health policy.

Data for this cross-sectional study were collected via face-to-face interaction following the pen-and-paper method. A total of 371 usable responses were received from randomly selected respondents of eight public sector universities located in the South Punjab province of Pakistan. Partial least squares structural equation modeling analysis was performed using SmartPLS program. A supplementary qualitative study, based on 21 in-depth interviews with the smokers, was conducted to augment findings of the quantitative study.

Results showed that protection motivation theory and theory of planned behaviour are supported to predict intentions to quit smoking. Subjective norms have a greater influence on intentions to quit smoking than attitudes towards smoking cessation, while perceived behavioural control fails to predict intentions to quit smoking. Perceived rewards and perceived cost are significant in predicting attitudes towards smoking cessation, while extrinsic rewards predict intentions to quit smoking.

The existing models reported in the literature have sparsely investigated the cognitive (such as motivation and emotions) and social factors (such as social influence and behavioural controls) together as determinants of intentions to quit smoking, leaving room for more studies on an integrated model of these factors. This study takes the opportunity and proposes an integrated model encompassing psycho-social factors to predict tobacco consumption quitting behaviour in an emerging economy context.

The purpose of this viewpoint is to discuss and analyze three major governance tools that have been implemented in the United States to target tobacco smoking as a major public health concern.

The author highlights the negative consequences of smoking as a global and U.S public health concern and discusses three categories of governance tools implemented in the U.S. Additionally, emerging challenges in the U.S. and different sides of story in developing countries are underscored.

Although some success has been reached in controlling smoking-related mortalities and morbidities in the U.S. and most of the countries, long-term and sustainable improvement require active surveillance and constant implementation of evidence-based policies and programs.

This viewpoint discusses the governance tools that can be implemented to decrease smoking-related preventable mortalities and morbidities. Similar tools with some tuning can be used to target smoking in other nations. Additionally, these tools can be modified to target other public health-related wicked problems such as obesity, alcohol consumption, and traffic accidents.

This viewpoint highlights the magnitude of smoking as a major public health concern and underscores the necessity of using governance tools in targeting this issue. Additionally, it provides application examples from the United States implementable in other countries with some contextual justifications and tuning.

For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis.

This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach.

The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students.

The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.

Although phone calls are widely used by debt collection services to persuade delinquent customers to repay, few financial services studies have analyzed the unstructured voice and text data to investigate how debt collection call strategies drive customers to repay. Moreover, extant research opens the “black box” mainly through psychological theories without hard behavioral data of customers. The purpose of our study is to address this research gap.

The authors randomly sampled 3,204 debt collection calls from a large consumer finance company in East Asia. To rule out alternative explanations for the findings, such as consumers' previous experience of being persuaded by debt collectors or repeated calls, the authors selected calls made to delinquent customers who had not been delinquent before and were being called by the company for the first time. The authors transformed the unstructured voice and textual data into structured data through automatic speech recognition (ASR), voice mining, natural language processing (NLP) and machine learning analyses.

The findings revealed that (1) both moral appeal (carrot) and social warning (stick) strategies decrease repayment time because they arouse mainly happy emotion and fear emotion, respectively; (2) the legal warning (stick) strategy backfires because of decreasing the happy emotion and triggering the anger emotion, which impedes customers' compliance; and (3) in contrast to traditional wisdom, the combination of carrot and stick fails to decrease the repayment time.

The findings provide a valuable and systematic understanding of the effect of carrot strategies, stick strategies and the combinations of them on repayment time. This study is among the first to empirically analyze the effectiveness of carrot strategies, stick strategies and their joint strategies on repayment time through unstructured vocal and textual data analysis. What's more, the previous studies open the “black box” through psychological mechanism. The authors firstly elucidate a behavioral mechanism for why consumers behave differently under varying debt collection strategies by utilizing ASR, NLP and vocal emotion analyses.

With the proliferation of objectionable materials (e.g. pornography, violence, drugs, etc.) available on the WWW, there is an urgent need for effective countermeasures to protect children and other unsuspecting users from exposure to such materials. Using pornographic Web pages as a case study, this paper presents a thorough analysis of the distinguishing features of such Web pages. The objective of the study is to gain knowledge on the structure and characteristics of typical pornographic Web pages so that effective Web filtering techniques can be developed to filter them automatically. In this paper, we first survey the existing techniques for Web content filtering. A study on the characteristics of pornographic Web pages is then presented. The implementation of a Web content filtering system that combines the use of an artificial neural network and the knowledge gained in the analysis of pornographic Web pages is also given.