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Using the combined theoretical umbrella of organizational legitimacy theory, service-dominant logic, fairness heuristic theory and two-factor theory, the purpose of this paper is to investigate the effectiveness of different food recall strategies (recall proactiveness and compensation) in terms of both how consumers react (perceived organizational legitimacy and purchase intention) and how recall norms would influence the effectiveness in three countries. In addition to the reporting of important results, this paper provides implications for food companies to handle effectively the recalls, especially when the recalls are cross-country.

A 2 compensation (high vs low) ×2 recall strategy (proactive vs passive) scenario experiment was conducted in Hong Kong, the USA and Mainland China. After checking the effectiveness of manipulation, the paper tested the main effect and interaction effect of recall proactiveness and compensation on perceived organizational legitimacy and purchase intention. In addition, the mediating effect of perceived organizational legitimacy between recall strategies and purchase intention was also tested.

Significant main effect, interaction and mediation effect were found across the three countries with a different pattern. For the USA and Mainland China which have strong recall norms, the interaction found followed the predictions of the two-factory theory. However, the pattern found in Hong Kong, which has weak recall norms, followed the predictions of the fairness heuristic theory. Full mediation effect of perceived organizational legitimacy between compensation and purchase intention was found in the USA and Mainland China, while it was only partial in Hong Kong. For the mediation between proactiveness and purchase intention, full mediation was found in Hong Kong and the USA, while it was only partial in Mainland China.

First, this study differentiated food recall strategy into two dimensions – recall proactiveness and compensation. Second, this study tested the applicability of two-factor theory and fairness heuristic theory in recalls by testing the competing hypotheses proposed according to the two theories. Finally, this study can further help our understanding of the recall effectiveness across different recall norms.

This study aims to analyse the effectiveness of a static promotional banner located on a hotel reservation website in terms of capturing the visitor’s visual attention by exploring how this impact depends on the user’s degree of internet experience.

An experiment was conducted using the eye-tracking methodology, in addition to a self-administered questionnaire. Through eye-tracking technology, eye movements were recorded whilst participants explored a generic hotel website. The factors used in the analyses were the position of the banner on the website and participants’ experience as internet users.

The findings showed that positioning a banner at certain locations on the webpage may lead to a better recall, which, in part, seems to result from the visual attention that such locations attract. The mediation analysis showed that the bottom-right and bottom-left positions have a negative effect on banner recall due, in part, to the shorter attention times and the smaller number of fixations those positions induce. Although the visitor’s level of internet experience affected his/her visual attention towards the banner, its impact on banner recall was non-significant. Results are discussed considering which variables produce greater effectiveness in capturing the user’s attention.

The paper draws several implications for the marketing literature, hospitality management and society in general.

The study is the first to analyse the impact of the position of a static ad on users’ visual attention and memory, considering the user’s degree of internet experience.

Este estudio analiza la eficacia de un banner promocional estático ubicado en un sitio web de reservas de un hotel en términos de captación de la atención visual del visitante examinando cómo este efecto depende del grado de experiencia del usuario en Internet.

Se llevó a cabo un experimento utilizando la metodología de seguimiento ocular (eye tracking), junto con un cuestionario autoadministrado. A través de la tecnología de eye tracking, se registraron los movimientos oculares mientras los participantes exploraban el sitio web genérico de un hotel. Los factores considerados en los análisis fueron la posición del banner en el sitio web y la experiencia de los participantes como usuarios de Internet.

Los hallazgos mostraron que colocar un banner en ciertas ubicaciones de la página web puede conducir a un mayor recuerdo, lo que, en parte, parece ser el resultado de la atención visual que atraen tales posiciones. El análisis de mediación mostró que las posiciones inferior-derecha e inferior-izquierda tienen un efecto negativo en el recuerdo del banner debido, en parte, a los tiempos de atención más cortos y al menor número de fijaciones que provocan esas posiciones. Aunque el nivel de experiencia de Internet del visitante afectó su atención visual hacia el banner, su impacto en el recuerdo del banner no fue significativo. Los resultados son discutidos considerando qué variables producen mayor efectividad a la hora de captar la atención del usuario.

El artículo presenta una serie de implicaciones para la literatura de marketing, la gestión hotelera y la sociedad en general.

Nuestro estudio es el primero en analizar el impacto de la posición de un anuncio estático en la atención visual y la memoria de los usuarios, considerando el nivel de experiencia del usuario en Internet.

本研究分析了位于酒店预订网站上的静态促销横幅在吸引访问者视觉注意力方面的效果, 并探索了这种效果如何受到用户的互联网体验程度的影响。

本文除了采用自填问卷的方式, 还使用眼动追踪方法进行了实验。通过眼动追踪技术, 记录了参与者在浏览一般酒店网站时的眼动情况。分析中使用的因素是网站上横幅的位置和参与者作为互联网用户的体验。

调查结果显示, 将横幅定位在网页上的某些位置可能会导致更佳的记忆, 这种效果在一定程度上是由于横幅的位置吸引了视觉注意力。调解分析表明, 横幅在右下角和左下角的位置对的回忆有负面影响, 部分原因是这些位置引起的注意时间较短、固定次数较少。虽然访问者的互联网体验水平影响了他/她对横幅的视觉注意力, 但其对横幅记忆的影响并不显著。讨论结果时考虑到了某些变量在吸引用户的注意力方面具有更明显的效果。

本文对市场营销文献、酒店管理和社会都具有一定的借鉴意义。

本次研究是第一个分析静态广告的位置对用户视觉注意力和记忆的影响的研究, 考虑到用户的互联网体验程度。

This study aims to examine the effect of game newness and game interactivity on players’ brand recall and brand attitude using contrast effect, mind-engagement and transfer effect theories.

A 2 (newness: congruent or incongruent) × 2 (game interactivity: high or low) between-subjects measures design was conducted. A total of 224 undergraduate management students participated in the study. A 2 × 2 between-subjects measures multivariate analysis of variance was used to test the hypotheses.

Findings show that incongruent-newness results in higher brand recall but less favorable brand attitude. Under incongruent-newness condition, high interactivity results in higher brand recall. However, under congruent-newness condition, both high- and low-interactivity conditions result in similar brand recall. Under congruent-newness condition, high interactivity results in more favorable brand attitude, whereas under incongruent-newness condition, both high- and low-interactivity conditions result in similar brand attitude.

Developing high brand recall rates and attitudes are the prime goals of advertisers for selecting a medium to promote their brands. This experimental study adds to the knowledge of online media advertising, especially in-game advertising (IGA) as a media-strategy to advertise brands taking newness and game-interactivity factors into consideration.

From the perspectives of attention, cognitive elaboration, engagement and transportation of experience, this study adds to the literature of IGA by examining the impact of newness and game interactivity.

Se analiza el efecto de la congruencia de la novedad del juego y su interactividad en el recuerdo y la actitud hacia la marca de los jugadores utilizando las teorías de contrast effect, mind-engagement y transfer effect.

Se desarrolló un diseño de 2 (novedad: congruente o incongruente) x 2 (interactividad de juego: alta o baja) de medidas entre sujetos. 224 estudiantes de administración participaron en el estudio. Para contrastar las hipótesis se utilizó un MANOVA de medidas entre sujetos de 2 x 2.

Los hallazgos muestran que cuando la novedad es incongruente es mayor el recuerdo de la marca, pero la actitud es menos favorable. Bajo la condición de novedad incongruente, la alta interactividad motiva mayor recuerdo de la marca. Sin embargo, en la condición de novedad congruente, tanto las condiciones de alta como las de baja interactividad resultan en el mismo nivel de recuerdo de marca. Si la novedad es congruente, la alta interactividad conduce a una actitud de marca más favorable, mientras que, en condiciones de novedad incongruente, tanto la alta como baja interactividad conducen a una actitud hacia la marca similar.

Lograr altos índices de recuerdo y actitudes positivas hacia la marca son los objetivos principales de los anunciantes al seleccionar un medio para anunciar sus marcas. Este estudio avanza en el conocimiento de la publicidad online, especialmente la publicidad en juegos como estrategia de medios para anunciar marcas teniendo en cuenta la novedad e interactividad de los juegos.

Desde las perspectivas de la atención, la elaboración cognitiva, el compromiso y la experiencia, este estudio contribuye a la literatura de la publicidad en juegos al examinar el impacto de la novedad y la interactividad de los juegos.

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.

This study explores the effect of video assistant referee (VAR) sponsorship on spectator response and compares it with advertising and conventional sponsorship.

An experiment with 809 subjects is conducted by analyzing 20 one-minute video clip stimuli from a Premier League soccer game divided into four formats: two formats of VAR sponsorship, advertising, and conventional sponsorship.

The results show that the indicators of recall, credibility, and perceived congruence improve when the VAR sponsorship format is used.

This is the first manuscript to examine the effectiveness of a new type of sponsorship: VAR sponsorship. This manuscript provides metrics that will guide practitioners on whether to use this type of sponsorship.

This study aims to explore the difference in cognitive strategy use during observed occupational performance between and within different levels of impaired awareness of deficits of individuals with acquired brain injury (ABI).

A cross-sectional study (N = 24) of individuals with ABI receiving rehabilitation and with the capacity to demonstrate goal-directed behaviour (Allen cognitive level screen score = 4.0) was undertaken. Cognitive strategy use during occupational performance of daily activities (measured with the perceive, recall, plan and perform [PRPP]) was evaluated between and within different awareness levels (awareness levels measured by the self-regulation skill interview). Statistical analyses, using independent t-test, Mann Whitney U test, ANOVA and Friedman test, were executed.

Significant differences were shown for both strengths and weaknesses in cognitive strategy use between emergent (n = 13) and anticipatory awareness (n = 11) groups on PRPP items “perceive”, “sensing” and “mapping”; and “searches”, “recall steps”, “identify obstacles”, “calibrates”, “stops”, “continues” and “persists”. Within emergent awareness group, participants scored lowest related to “perceive”, “plan”, “sensing”, “mapping”, “programming” and “evaluating”. Within anticipatory awareness group, participants scored lowest related to “plan”, “programming” and “evaluating”.

This study showed differences in cognitive strategy application during task performance in individuals with emergent or anticipatory awareness deficits that fit with theoretical expectations. It is recommended to make use of the PRPP assessment results (strengths and weaknesses in cognitive strategy application) to support the level of awareness determination. The PRPP assessment results and the level of awareness tailor the clinical reasoning process for personalised intervention planning and cognitive strategy training.

Because impaired awareness has so much impact on the course and outcome of rehabilitation (Rotenberg-Shpigelman et al., 2014), in clinical practice, it is of paramount importance to be aware of the level of awareness of the client (Smeets et al., 2017) and the effect on occupational performance.

Classification techniques have been applied to many applications in various fields of sciences. There are several ways of evaluating classification algorithms. The analysis of such metrics and its significance must be interpreted correctly for evaluating different learning algorithms. Most of these measures are scalar metrics and some of them are graphical methods. This paper introduces a detailed overview of the classification assessment measures with the aim of providing the basics of these measures and to show how it works to serve as a comprehensive source for researchers who are interested in this field. This overview starts by highlighting the definition of the confusion matrix in binary and multi-class classification problems. Many classification measures are also explained in details, and the influence of balanced and imbalanced data on each metric is presented. An illustrative example is introduced to show (1) how to calculate these measures in binary and multi-class classification problems, and (2) the robustness of some measures against balanced and imbalanced data. Moreover, some graphical measures such as Receiver operating characteristics (ROC), Precision-Recall, and Detection error trade-off (DET) curves are presented with details. Additionally, in a step-by-step approach, different numerical examples are demonstrated to explain the preprocessing steps of plotting ROC, PR, and DET curves.

This study aims to explore how to deanonymize cryptocurrency money launderers with the help of machine learning (ML). Money is laundered through cryptocurrencies by distributing funds to multiple accounts and then reexchanging the crypto back. This process of exchanging currencies is done through cryptocurrency exchanges. Current preventive efforts are outdated, and ML may provide novel ways to identify illicit currency movements. Hence, this study investigates ML applicability for combatting money laundering activities using cryptocurrency.

Four supervised-learning algorithms were compared using the Bitcoin Elliptic Dataset. The method covered a quantitative analysis of the algorithmic performance, capturing differences in three key evaluation metrics of F1-scores, precision and recall. Two complementary qualitative interviews were performed at cryptocurrency exchanges to identify fit and applicability of the algorithms.

The study results show that the current implemented ML tools for preventing money laundering at cryptocurrency exchanges are all too slow and need to be optimized for the task. The results also show that while not one single algorithm is most suitable for detecting transactions related to money-laundering, the specific applicability of the decision tree algorithm is most suitable for adoption by cryptocurrency exchanges.

Given the growth of cryptocurrency use, this study explores the newly developed field of algorithmic tools to combat illicit currency movement, in particular in the growing arena of cryptocurrencies. The study results provide new insights into the applicability of ML as a tool to combat money laundering using cryptocurrency exchanges.

In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users.

This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users.

The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is.

This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.

Indian Premier League (IPL) is one of the more popular cricket world tournaments, and its financial is increasing each season, its viewership has increased markedly and the betting market for IPL is growing significantly every year. With cricket being a very dynamic game, bettors and bookies are incentivised to bet on the match results because it is a game that changes ball-by-ball. This paper investigates machine learning technology to deal with the problem of predicting cricket match results based on historical match data of the IPL. Influential features of the dataset have been identified using filter-based methods including Correlation-based Feature Selection, Information Gain (IG), ReliefF and Wrapper. More importantly, machine learning techniques including Naïve Bayes, Random Forest, K-Nearest Neighbour (KNN) and Model Trees (classification via regression) have been adopted to generate predictive models from distinctive feature sets derived by the filter-based methods. Two featured subsets were formulated, one based on home team advantage and other based on Toss decision. Selected machine learning techniques were applied on both feature sets to determine a predictive model. Experimental tests show that tree-based models particularly Random Forest performed better in terms of accuracy, precision and recall metrics when compared to probabilistic and statistical models. However, on the Toss featured subset, none of the considered machine learning algorithms performed well in producing accurate predictive models.