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This study aims to propose a model of publics' ethical activism, testing the role of emotional outrage in an extended framework of the previously established STOPS model. Thus, this study aims to investigate (1) how ethical perception of a social issue affects situational motivation that leads to participation in public activism, and (2) how emotional outrage plays a role in mediating between situational motivation and activism behaviors.

The study aims at investigating the mediating role of emotional outrage between situational motivation and activism behaviors, which have not been investigated thoroughly in public relations research. By conducting a national survey with 386 people (N = 386) living in Australia, the study's suggestive model was tested in the context of two ethical issues (e.g. climate change and anti-racism). This study found that people who perceive an ethical issue are likely to be motivated to participate in activism behaviors. Specifically, three situational perceptions (i.e. problem recognition, constraint recognition and involvement recognition) were found to be significant factors affecting situational motivation (SM) in problem solving. A high level of emotional outrage was found to play a mediating role between SM and consequential ethical activism behaviors. The more people feel outraged about an ethical issue, the more likely they will engage in punitive behavior. This study contributes to the theoretical development of public relations by illuminating how situational perceptions lead to emotional outrage that promotes behavioral intentions in an ethical context. This study also suggests that a practitioner should be sought to manage the levels of perceptual factors when setting up a communication plan in response to an ethical public crisis.

This study found that people who perceive an ethical issue are likely to be motivated to participate in activism behaviors. Specifically, three situational perceptions (i.e. problem recognition, constraint recognition and involvement recognition) were found to be significant factors affecting situational motivation (SM) in problem solving. A high level of emotional outrage was found to play a mediating role between SM and consequential ethical activism behaviors. The more people feel outraged about an ethical issue, the more likely they will engage in punitive behavior.

This study substantiates how three perceptual antecedents may conjointly affect situational motivation. Also, the findings in this study also contribute to theoretical development in predicting ethical activism intentions. Another contribution of this study is to demonstrate the mediating role of emotional outrage between situational motivation and ethical activism. The authors strived to explore individuals' perceptions and its impact on intention to boycott against ethical problematic social issues. It should be considered that this study used a hypothetical and manipulated situation where respondents are exposed to the visual stimuli that focus on the moral problems that those specific issues have raised.

First, as problem recognition plays a prime mover role in the overall problem-solving process in an ethical context, organizations should reduce individual's ethical problem recognition. When setting up a communication plan, an organization should showcase their positive role in addressing the ethical problems (e.g. to show Adani's commitment to indigenous people and efforts to protect the environment). The response strategy should be effective enough to create an “ethical dilemma,” which refers to a situation in which one has a difficult choice to make between two ethical options.

This study contributes to theoretical development of public relations by illuminating how situational perceptions lead to emotional outrage that promotes behavioral intentions in an ethical context. This study also suggests that a practitioner should be sought to manage the levels of perceptual factors when setting up a communication plan in response to an ethical public crisis.

Since 1910, when John Dewey first introduced the five‐stage decision process, it has been a widely accepted concept and still serves as the central pillar of a popular consumer behavior model. These stages are Problem Recognition, Information Search, Alternative Evaluation, Choice, and Outcomes. The importance of these stages is attested to by the considerable attention devoted to most of them in numerous textbooks and journal articles. Such attention, however, has not come to the Problem Recognition stage. While some texts provide hypothetical descriptions of this “trigger” of the decision process, theoretical discussion and empirical support are surprisingly lacking. Journal literature fares even worse, with articles on the topic almost non‐existent. Lack of information on the topic is even more ironic when one considers that a purchase cannot occur unless a problem is recognized! The purpose of this article is to provide a detailed explanation of the Problem Recognition process. The results of the few empirical studies that have been done will be examined. In addition, a proposed model of the Problem Recognition process is presented. The implications of this material will be discussed as it relates to marketing.

Online gaming is a global phenomenon that can lead to behavioural addiction and affect players’ mental and physical health. This paper aims to integrate the concepts of help-seeking and stages of change to investigate triggers for problem recognition for problematic online gaming that lead to help-seeking behaviour.

Critical Incident Technique method was used to collect a total of 78 critical incidents from a sample of 12 male online gamers who self-identified as having experienced problematic online gaming behaviour.

Six classifications of problem recognition triggers for young male problematic online gamers were identified: self-realisation, negative consequences, negative emotions, social influence, competing priorities and impact on social skills. Results indicate that both positive and negative triggers are important for problem recognition.

Valuable contributions were made to the social marketing literature by presenting an integrated model of help-seeking and stages of change theories, providing new insights into SOC and expanding the understanding of the processes involved in the transition between pre-contemplation and contemplation.

Introduction With the publication of the consumer behaviour textbook by Engel, Kollat and Blackwell (1968), the dominant approach to studying consumer behaviour has been to study the elements of the consumer behaviour decision process. The approach taken by Engel, Kollat and Blackwell, and since then by many other researchers, is that this process is an exercise in problem solving and that a problem exists when there is a goal to be attained or sought and uncertainty as to what is the best solution for the given or perceived problem (Markin, 1974).

Within the existing health care system, a significant percentage of Americans over the age of 65 will have need of extended health care. Yet the extended care industry has little information as to how consumers will make a choice among placement options. The results of this study describe both the need recognition and pre‐selection search stages of the decision process and the impact of need recognition on subsequent search activity. The information obtained from an influential person as the problem is being defined appears to have a complex relationship with pre‐selection search, with initial information leading to less search but additional information increasing search activity. Contrary to previous studies, pre‐selection search was found to be fairly extensive, increasing with time availability.

In this research, neural network (NN) and genetic algorithm (GA) are used together to design optimal NN structure. The proposed approach combines the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications in design and manufacturing. Genetic input selection approach is introduced to obtain optimal NN topology. Experimental results are given to evaluate the performance of the proposed system.

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

One important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that for data smaller network structure, smaller learning rate and momentum are required. The relative sample size also has a considerable effect on the performance of the classifier. Further studies suggest that classification accuracy is achieved with the confusion matrix of the data used. Hence, with these results the proposed system can be used efficiently for more objects. Depending upon the manufacturing product and process used, the dimension verification and surface roughness may be integrated with proposed technique to develop a comprehensive vision system. The proposed technique is also highly suitable for web inspections, which do not require dimension and roughness measurement and where desired accuracy is to be achieved at a given speed. In general, most recognition problems provide identity of object with pose estimation. Therefore, the proposed recognition (pose estimation) approach may be integrated with inspection stage.

This paper considers the problem of recognizing and classifying the objects of such mechanical part. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. ANN is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

The concept of a knowledge interface for library users is developed as an extension of intelligent knowledge‐base system (IKBS) concepts. Contemporary directions in intelligent decision support, particularly in the role of search intermediaries, are then examined to identify the significance of intelligent intermediaries as a solution to unstructured decision support requirements of library users. A DISCOURSE SCRIPT is given to illustrate one form of intelligent intermediary.

In order to answer why and how some individuals identify opportunities, the purpose of this paper is to offer a comprehensive framework of key effective factors that investigates the role of prior knowledge and cognitive characteristics of entrepreneurial alertness and learning in the entrepreneurial opportunities recognition process.

First, the authors identified key effective factors and developed research hypotheses and conceptual model. Second, the authors considered research methodology including sampling and data collection, measurement model. The third section presents a report of the results from the analysis of the structural model and the hypotheses tests.

The findings show that all the three factors had positive impact on opportunity recognition. It is also confirmed that prior knowledge has significant impact on entrepreneurial alertness and learning. Finally, the results demonstrate that both entrepreneurial alertness and learning partially mediated the relationship between prior knowledge and opportunity recognition.

Possible effects of other cognitive characteristics such as creativity and innovation can be considered in the model to increase accuracy of the findings. Also, comparative studies with the proposed framework in different industrial domains can be conducted in order to compare the possible differences concerning the role of the factors in opportunity recognition.

By using this model, entrepreneurs will be able to identify and enhance the factors that influence opportunity recognition, thus honing this entrepreneurial ability. Also, this framework can be employed in the field of education to identify opportunity recognition problems in individuals and to foster entrepreneurs.

This paper presents a new integrated model that would examine the impact of key effective factors of entrepreneurial opportunity recognition simultaneously while taking into account the interactions among them.

A real potential exists for library use of voice technologies: as aids to the disabled or illiterate library user, as front‐ends for general library help systems, in online systems for commands or control words, and in many of the hands‐busy‐eyes‐busy activities that are common in libraries. Initially, these applications would be small, limited processes that would not require the more fluent human‐machine communication that we might hope for in the future. Voice technologies will depend on and benefit from new computer systems, advances in artificial intelligence and expert systems to facilitate their use and enable them to better circumvent present input and output problems. These voice systems will gradually assume more importance, improving access to information and complementing existing systems, but they will not likely revolutionize or dominate human‐machine communications or library services in the near future.