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The purpose of this paper is to conceptualize several dimensions of product‐price knowledge and to develop measurement variables that qualify a person's product‐price knowledge. Assuming product‐price knowledge is a multidimensional construct, the relationship between its dimensions is to be investigated and consumer characteristics are to be analyzed according to whether they influence these dimensions.

Drawing on existing research, the paper theoretically develops a framework that proposes a taxonomy of product‐price knowledge and leads to a road map to identify determinants that probably affect a person's product‐price knowledge. Applying data on 319 shoppers, a path model simultaneously estimates the internal structure of the specified dimensions of product‐price knowledge, and determines the influence of the selected consumer characteristics on product‐price knowledge.

The proposed taxonomy sees product‐price knowledge as encompassing not just isomorphic prices, i.e. actually or formerly perceived and recalled prices. Rather, inferential prices, such as the normal price, or the upper reservation price for a product, as well as knowledge about price‐setting conditions and confidence in memorized prices, comprise important elements of a person's product‐price knowledge. As a result, there are several measurement variables to qualify a person's product‐price knowledge. The empirical results identify price mavenism, price consciousness, the use of a shopping list, and shopping frequency, as determinants of the accuracy and size of, and confidence in, one's product‐price knowledge, even though the impact structure is not uniform. There are some indications that formerly encountered price stimuli represent a relatively obsolete part of a consumer's product‐price knowledge.

Research on product‐price knowledge should not be restricted to measuring the accuracy of recalled paid prices. Size of product‐price knowledge, and confidence in one's price knowledge, are identified as measures that are at least equally important to qualify a person's product‐price knowledge as accuracy. Furthermore, size and confidence are also additional dependent variables in analyzing determinants of consumers' product‐price knowledge. This is important for researchers and practitioners.

The proposed taxonomy and the empirical results lead to finer grained understanding of product‐price knowledge as a multi‐dimensional construct and its determinants.

The prevalence of foodborne illness remains high in Australia. In response, government initiatives have been implemented to inform consumers of ways to safely handle food. The aim of this study was to examine the accuracy of prompted and unprompted recall of messages from a safe food-handling media campaign in Western Australia, and whether this accuracy of prompted and unprompted recall differed by demographic factors and the mode of delivery of the campaign materials.

Survey responses from 121 participants (M_age = 47.15 years, SD = 15.52) who reported seeing or hearing the campaign were analysed. A series of chi-square tests were used to determine the accuracy of recall when prompted and unprompted, and the accuracy of unprompted and prompted recall across demographic factors and mode of delivery.

Results indicated that more participants accurately recalled the campaign messages when prompted (66.1%) compared to unprompted (35.5%), when they had seen outdoor advertisements (e.g. at bus stops or in shopping malls), and if they were between 30 and 45 years of age.

This study is the first to explore the uptake and comprehension of messages from a safe food-handling media campaign. Evaluation of safe food-handling media campaigns has shown some efficacy in relation to behaviour change; however, little is known about the uptake or comprehension of the campaign messages, and factors that may influence this.

Although it has been proposed that recall processes play a role in the retail sales effects of 9‐ending pricing, substantial effects of price endings on the level of recalled prices has not been demonstrated. With an improved testing procedure, it is found that the level of a set of prices with low ending digits (such as 1 or 2 in the dollars place) is more likely to be overestimated in recall than the level of equivalent sets of prices with high ending digits (such as 6, 7, or 9 in the dollars place). The results of the study support the role of left‐to‐right processing of price information and point out some consequences for retailers of the use of low numbers in price‐ending digits.

Balance Theory has accumulated an impressive record of empirical confirmation at both the micro- and macro-levels. Yet, it is unclear why humans consistently prefer balanced relations when imbalance offers the opportunity to reap material rewards. We argue that balance is preferred because it functions as a “compression heuristic,” allowing networks to be more easily encoded in, and recalled from, memory.

We present the results of a novel randomized laboratory experiment using nearly 300 subjects. We evaluate the independent and joint effects of degree of balance/imbalance and presence/absence of kin compression heuristics on network recall.

We find that memory for relationship valence is more accurate for balanced, rather than imbalanced, networks and that relationship existence and relationship valence are separable cognitive elements. We also use comparisons between kin and non-kin networks to suggest that humans are implicitly aware of the conditions under which imbalanced networks will be most durable.

We show that the tension/strain postulated to generate mental and behavioral responses to increase balance likely stems from cognitive limitations. More broadly, this connects balance theory to models of human cognition and evolution and suggests that human general processing ability may have evolved in response to social, rather than physical, challenges.

The purpose of this study isto synthesize recent empirical research investigating memory of stressful critical incidents (both simulated and occurring in the field) among law enforcement officers.

The study used the approach of systematic state-of-the-art review.

In total, 20 studies of police and military officers show reduced detail and accuracy of high- versus low-stress incidents, especially for peripheral versus target information. Decrements in memory performance were mediated by the extent of physiological stress responses. Delayed recall accuracy was improved among officers that engaged in immediate post-incident rehearsal, including independent debriefing or reviewing body-worn camera footage.

Most studies were not found through systematic database searches, highlighting a need for broader indexing and/or open access publishing to make research more accessible.

By understanding how stress physiology enhances or interferes with memory encoding, consolidation and recall, evidence-based practices surrounding post-incident evidence gathering are recommended.

The current review addresses common public misconceptions of enhanced cognitive performance among police relative to the average citizen.

The current work draws from scientific knowledge about the pervasive influence of stress physiology on memory to inform existing practices surrounding post-incident evidence gathering among police.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

The purpose of this study was to explore potential witnesses' memories for a day that was experienced an unremarkable. There may be instances in an investigation in which all leads have been exhausted, and investigators use a broad appeal for witnesses who may have witnessed something important. Investigators can benefit from knowing the types of information that may be recalled in such circumstances, as well as identifying specific methods that are effective in eliciting useful information.

The present study explored how the delay to recall and recall method influenced the recollection of a seemingly unremarkable day that later became important. Participants were asked to recall an experienced event that occurred either recently (a few weeks prior) or in the distant past (a year prior). Participants recalled via either a written method, in-person individual-spoken or collaborative-spoken interviews.

Results suggest an independent benefit for individual-spoken in-person recall (compared to written or collaborative-spoken recall) and recall undertaken closely after an event (compared to delayed recall). Both individual-spoken interviews as well as more recent recollection resulted in a greater number of overall details recalled. The authors further examined the types of details recalled that might be important to progressing an investigation (e.g. other witnesses and records).

The present work provides important implications for interviewing witnesses about a seemingly unremarkable event that later became important.

This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and thus the documents available online become countless. The text documents comprise of research article, journal papers, newspaper, technical reports and blogs. These large documents are useful and valuable for processing real-time applications. Also, these massive documents are used in several retrieval methods. Text classification plays a vital role in information retrieval technologies and is considered as an active field for processing massive applications. The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents. There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifier.

At first, the input documents are pre-processed using the stop word removal and stemming technique such that the input is made effective and capable for feature extraction. In the feature extraction process, the features are extracted using the vector space model (VSM) and then, the feature selection is done for selecting the highly relevant features to perform text categorization. Once the features are selected, the text categorization is progressed using the deep belief network (DBN). The training of the DBN is performed using the proposed grasshopper crow optimization algorithm (GCOA) that is the integration of the grasshopper optimization algorithm (GOA) and Crow search algorithm (CSA). Moreover, the hybrid weight bounding model is devised using the proposed GCOA and range degree. Thus, the proposed GCOA + DBN is used for classifying the text documents.

The performance of the proposed technique is evaluated using accuracy, precision and recall is compared with existing techniques such as naive bayes, k-nearest neighbors, support vector machine and deep convolutional neural network (DCNN) and Stochastic Gradient-CAViaR + DCNN. Here, the proposed GCOA + DBN has improved performance with the values of 0.959, 0.959 and 0.96 for precision, recall and accuracy, respectively.

This paper proposes a technique that categorizes the texts from massive sized documents. From the findings, it can be shown that the proposed GCOA-based DBN effectively classifies the text documents.

Eyewitness testimony can determine the outcome of criminal investigations. The cognitive interview (CI) has been widely used to collect informative and accurate accounts. However, face-to-face interviews have been restricted during the current pandemic, raising the need for using video-conferencing. The authors tested whether virtual interviews could produce elaborate accounts from eyewitnesses and if the CI superiority effect against a structured interview (SI) could be fully replicated online.

The authors used a 2 × 2 factorial design with interview condition (CI vs SI) and environment (face-to-face vs virtual) manipulated between-subjects. A total of 88 participants were randomly assigned to one of the four conditions. Participants watched a mock robbery and were interviewed 48 h later using either the SI or the CI. Both interviews contained the same structure and interview phases but only the CI included its key cognitive mnemonics/ instructions. Both sessions were either face-to-face or online.

Participants interviewed with the CI recalled more information than participants interviewed with the SI, regardless of the interview environment. Both environments produced a comparable amount of recall. Report accuracy was high for all groups.

This can be crucial to inform police practices and research in this field by suggesting investigative interviews can be conducted virtually in situations such as the current pandemic or when time and resources do not allow for face-to-face interviewing.

To the best of the authors’ knowledge, this is the first study showing that the CI superiority effect can be replicated online and that a fully remote CI can produce elaborate accounts.

Longitudinal facial cracks (LFC) are one of the major defects occurring in the continuous-casting stage of thin slab caster using funnel molds. Longitudinal cracks occur mainly owing to non-uniform cooling, varying thermal conductivity along mold length and use of high superheat during casting, improper casting powder characteristics. These defects are difficult to capture and are visible only in the final stages of a process or even at the customer end. Besides, there is a seasonality associated with this defect where defect intensity increases during the winter season. To address the issue, a model-based on data analytics is developed.

Around six-month data of steel manufacturing process is taken and around 60 data collection point is analyzed. The model uses different classification machine learning algorithms such as logistic regression, decision tree, ensemble methods of a decision tree, support vector machine and Naïve Bays (for different cut off level) to investigate data.

Proposed research framework shows that most of models give good results between cut off level 0.6–0.8 and random forest, gradient boosting for decision trees and support vector machine model performs better compared to other model.

Based on predictions of model steel manufacturing companies can identify the optimal operating range where this defect can be reduced.

An analytical approach to identify LFC defects provides objective models for reduction of LFC defects. By reducing LFC defects, quality of steel can be improved.