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Breakfast consumption is associated with a range of beneficial health outcomes including improved overall diet quality, lower BMI, decreased risk of chronic disease, and improved cognitive function. Although there are many models of health and social behaviour, there is a paucity of research utilising these in breakfast consumption and very few studies that directly compare these models. This study aims to compare the theory of planned behaviour (TPB) and the health action process approach (HAPA) in predicting breakfast consumption.

University students (N=102; M=19.5 years) completed a questionnaire measuring demographics, TPB and HAPA motivational variables, and intentions. Behaviour and HAPA volitional variables were measured four weeks later.

Using structural equation modelling, it was found that the TPB model was a superior fit to the data across a range of model indices compared to the HAPA. Both models significantly predicted both intentions and behaviour at follow up; however, the TPB predicted a higher proportion of the variance in breakfast consumption (47.6 per cent) than the HAPA (44.8 per cent). Further, the volitional variables did not mediate the intention-behaviour gap, and the data were not an adequate statistical fit to the model compared to the TPB.

The results support the use of the TPB and show that some aspects of the HAPA are useful in predicting breakfast consumption, suggesting that risk perception and self-efficacy be targeted in interventions to increase behaviour. The volitional variables did not appear to mediate breakfast consumption indicating that intention is still the strongest predictor, at least in this behaviour.

The current study is the first to compare the TPB and HAPA in predicting breakfast consumption.

The theory of planned behaviour (TPB) considers the interrelationship between such concepts as beliefs, attitudes, norms, intentions and behaviour (Ajzen, 1991; Ajzen and Fishbein, 1975). Based on a review of academic sources, this paper aims to analyse the efficacy of the TPB for predicting people’s intentions when choosing a travel destination.

Surprisingly, only 15 studies were identified that used TPB to predict the choice of travel destination, though the theory has been used in other areas of tourism analysis.

Mixed results were found in the studies. Therefore, the adequacy of the TPB for predicting travellers’ intentions of choosing a destination may be questioned. However, there is nothing in the TPB suggesting that all the constructs of the model must contribute equally, significantly and simultaneously to behavioural intentions.

To achieve a more comprehensive understanding of the intentions in question, the TPB model may have to be extended to suit different settings. The decision-making process of choosing a destination is a complicated one; therefore, researchers’ attention should not only consider travellers’ intentions but also the direct effect of intentions on the actual behaviour.

Aggression has been introduced as one of the serious problems in public health. The purpose of this paper is to evaluate the ability of the extended theory of planned behavior (TPB) to predict the physical and verbal aggression behavior.

In this research, 462 teenagers were evaluated through the demographic questionnaire along with the main structures of the TPB as a predictor of behavior. After one month of follow-up, physical and verbal aggression was evaluated. Demographic data were analyzed descriptively by SPSS21 and predictability of the structures for intention and behavior of the physical and verbal aggression was analyzed by AMOS.

Mean and standard deviation of participants’ age were 14.70 and 1.12 years, respectively. In this research, 22.5% of the participants did not show physical aggression over the last one month and 20% of them did not show verbal aggression over the last month. Path analysis revealed that the variables of the TPB predicted 61% and 32% of variance of intention and physical aggression behavior, respectively, while these variables could describe 43% and 22% of the variance of intention and verbal aggression behavior, respectively. All of the concepts could be significant predictors of the behaviors. Subjective norms were the best predictor of the intention for physical and verbal aggression. Intention and perceived behavioral control were good predictors of physical and verbal aggression.

Given the role of subjective norms in intention and also the role of intention and perceived behavioral control of people for aggression, it can be concluded that emphasis on social and psychological education about subjective norms, peer groups and self-control can help reduce this problem.

A few studies have predicted behavior occurrence in the future. Given the lack of focus on the role of constructs that may bring about future behaviors, the current research was conducted to use the structures of the TPB to predict behavioral intention as well as perpetration of physical and verbal aggressive behaviors, independently.

There has been contradictory evidence as to whether implicit attitudes are more indicative of food consumption behavior than explicit attitudes. The purpose of this paper is to clarify the predictive validity of implicit attitudes for food consumption behaviors with two popular indirect measures – the implicit association test (IAT) and the affective misattribution procedure (AMP).

The authors examined the predictive validity of the IAT and AMP for focal and incidental food consumption behaviors (n=277).

Results revealed that the IAT and the AMP were more context-dependent than initially expected. The IAT only predicted incidental consumption behaviors in Study 1, and the AMP only predicted incidental consumption behaviors when preceding the IAT. However, the indirect measures provided unique variance for predicting incidental consumption behaviors. Only a direct, self-report measure predicted focal behaviors.

These findings suggest that both the AMP and the IAT can predict incidental consumption behaviors, but the presence and strength of these effects may be moderated by unsuspected variables such as task order.

The current study provides evidence for the benefits of utilizing implicit measures in addition to self-report measures during consumer and market research.

This research reevaluates the predictive validity of the IAT and AMP for food consumption behaviors and employs two measures of food consumption behaviors.

Although researchers have suggested that aggression is multiply determined, most studies examine only a small set of predictors, focusing on either situational or individual or reciprocal motives. Research has not studied extensively the relative strength of multiple antecedent sets. Using questionnaire data (n = 366), the current study examines eleven antecedents of employees engaging in aggression: situational antecedents (i.e., procedural, distributive, and interpersonal justice; organizational, work group, and job related stress), individual difference antecedents (i.e., Type A behavior, trait anger, reactions to anger), and reciprocal effects (i.e., being the target of aggression). Individual difference antecedents and being the target of aggression influence the frequency with which employees report engaging in aggression. Situational antecedents are not significant predictors once other antecedents are taken into account.

The new product development process comprises a series of information‐gathering phases intended to reduce the uncertainty which surrounds the management of innovation. To the extent that this is a rational process, new product failures can be attributed to a lack of high quality, relevant information for decision making. In view of the particularly high failure rate for consumer non‐durables, it makes sense to look critically at the quality of market research information employed in new product decision making. Concept and product tests, which rely heavily on measures of attitude and intention, are very frequently used to gain such information relatively early in the innovative process, but, while they have sometimes been indicted for their inability to predict managerially‐useful aspects of new brand choice, there appears to have been no attempt at understanding why they are often ineffective. Without this understanding it is impossible to suggest an alternative approach. There is no panacea for the problem of predicting consumer choice in new product development but there is great need to come to grips with this problem. This article attempts to provide the necessary understanding and suggests an alternative means of conceptualising the attitude — intentions — behaviour relationship in marketing.

This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business.

The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning; fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables; and sixth, providing appropriate strategies based on the proposed model.

According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models.

The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries; therefore, generalizing the results to other business centers should be used with caution.

Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Business owners must accept the items returned by the customers for any reasons, and the conditions for accepting returned items and the deadline for accepting the returned items must be clearly communicated to the customers. Store owners must consider a discount for a certain amount of purchase from the store. They have to use an exponential rule to increase the discount when the amount of purchase is increased to encourage customers for more purchase. The managers of large stores must try to quickly deliver the ordered items, and they should use equipped and new transporting vehicles and skilled and friendly workforce for delivering the items. It is recommended that the types of services, the rules for prizes, the discount, the rules for accepting the returned items and the method of distributing the items must be prepared and shown in the store for all the customers to see. The special services and reward rules of the store must be communicated to the customers using new media such as social networks. To predict the customer behaviors based on the data, the future researchers should use the boosting method because it increases efficiency and accuracy of prediction. It is recommended that for predicting the customer behaviors, particularly their churning status, the ANN method be used. To extract and select the important and effective variables influencing customer behaviors, the discriminant analysis method can be used which is a very accurate and powerful method for predicting the classes of the customers.

The current study tries to fill this gap by considering five basic and important variables besides RFM in stores, i.e. prize, discount, accepting returns, delay in distribution and the number of items, so that the business owners can understand the role services such as prizes, discount, distribution and accepting returns play in retraining the customers and preventing them from churning. Another innovation of the current study is the comparison of machine-learning methods with their boosting and bagging versions, especially considering the fact that previous studies do not consider the bagging method. The other reason for the study is the conflicting results regarding the superiority of machine-learning methods in a more accurate prediction of customer behaviors, including churning. For example, some studies introduce ANN (Huang et al., 2010; Hung and Wang, 2004; Keramati et al., 2014; Runge et al., 2014), some introduce support vector machine ( Guo-en and Wei-dong, 2008; Vafeiadis et al., 2015; Yu et al., 2011) and some introduce DT (Freund and Schapire, 1996; Qureshi et al., 2013; Umayaparvathi and Iyakutti, 2012) as the best predictor, confusing the users of the results of these studies regarding the best prediction method. The current study identifies the best prediction method specifically in the field of store businesses for researchers and the owners. Moreover, another innovation of the current study is using discriminant analysis for selecting and filtering variables which are important and effective in predicting churners and non-churners, which is not used in previous studies. Therefore, the current study is unique considering the used variables, the method of comparing their accuracy and the method of selecting effective variables.

This paper aims to predict the behaviour of the vehicles in a mixed driving scenario. This proposes a deep learning model to predict lane-changing scenarios in highways incorporating current and historical information and contextual features. The interactions among the vehicles are modelled using long-short-term memory (LSTM).

Predicting the surrounding vehicles' behaviour is crucial in any Advanced Driver Assistance Systems (ADAS). To make a decision, any prediction models available in the literature consider the present and previous observations of the surrounding vehicles. These existing models failed to consider the contextual features such as traffic density that also affect the behaviour of the vehicles. To forecast the appropriate driving behaviour, a better context-aware learning method should be able to consider a distinct goal for each situation is more significant. Considering this, a deep learning-based model is proposed to predict the lane changing behaviours using past and current information of the vehicle and contextual features. The interactions among vehicles are modeled using an LSTM encoder-decoder. The different lane-changing behaviours of the vehicles are predicted and validated with the benchmarked data set NGSIM and the open data set Level 5.

The lane change behaviour prediction in ADAS is gaining popularity as it is crucial for safe travel in a mixed driving environment. This paper shows the prediction of maneuvers with a prediction window of 5 s using NGSIM and Level 5 data sets. The proposed method gives a prediction accuracy of 97% on average for all lane-change maneuvers for both the data sets.

This research presents a strategy for predicting autonomous vehicle behaviour based on contextual features. The paper focuses on deep learning techniques to assist the ADAS.

This study aims to examine the applicability of an extended version of the theory of planned behavior (TPB) in predicting pro-environmental behavior, specifically the purchase behavior (PB) of package-free bath products, among students in higher education institutions (HEIs).

Using a non-experimental survey research design, this study empirically tested an extended TPB model through structural equation modeling. The dataset was obtained through a survey of undergraduate students in three HEIs in the Philippines.

Environmental knowledge (EK) predicted attitudes toward purchasing package-free bath products. Attitudes, subjective norms and pro-environmental self-identity (PSI) predicted intention to purchase package-free bath products. Furthermore, the intention to purchase package-free bath products and perceived behavioral control predicted PB of the said item.

The results imply that the addition of EK and PSI to the TPB is applicable in predicting pro-environmental behavior, specifically the purchase of package-free bath products.

The results showed how HEIs can encourage their students to purchase package-free bath products.

The results highlight how social and economic factors play a role in promoting or inhibiting pro-environmental behavior among HEI students.

The findings support the inclusion of EK and PSI to the TPB for an integrative model that aims to improve the prediction of the purchase of package-free bath products.

The purpose of this study is to examine gender differences in personality predictors of a specific form of workplace aggression: counterproductive work behaviors directed at individuals (CWB‐I).

Students (n=212) who were part‐time employees working at least 15 hours per week completed a measure of the five‐factor model (FFM) personality traits and two circumplex personality traits (Calmnesss and Pleasantness), as well as a measure of CWB‐I. Hierarchical regressions and tests of mean differences were used to examine hypotheses pertaining to gender differences in personality predictors of interpersonal aggression.

Results generally supported the hypotheses as shown by the significant interactions between gender and personality traits in predicting CWB‐I. Agreeableness and Pleasantness significantly (negatively) predicted CWB‐I among males, but not females. Emotional Stability significantly (negatively) predicted CWB‐I among females, but not males.

The use of self‐report surveys may impact the results of this study. However, as this is the first study to explore the complex interactions between gender and personality in predicting workplace aggression, it is hoped that future research tests these relationships with alternate samples and methodologies.

The results show that personality traits predict interpersonal workplace aggression differentially for males and females. Results also show that circumplex intersection traits are a useful supplement to the FFM traits in explaining interpersonal aggression in the workplace.

To the authors' knowledge, this is the first study to show that personality traits differentially predict interpersonal aggression for males and females; and to demonstrate the incremental validity of circumplex traits over FFM traits in predicting interpersonal aggression.