Information Processing Framework Research Articles

How foodstagramming posts influence restaurant visit intention: the mediating role of goal relevance and mimicking desire

PurposeThis study aims to explore the role of social media visual posts (known as foodstagramming) on restaurant visit intention. Drawing on the heuristic–systematic model and normative focus theory, this research introduces a framework that assesses the effects of key foodstagramming attributes – vicarious expression, aesthetic appeal and post popularity – and the mediating roles of goal relevance and mimicking desire, in the process.Design/methodology/approachStructural equation modelling was performed to test the proposed model using a sample of tourists (n = 377) and residents (n = 341). Multi-group analysis was performed to compare the differences between these groups.FindingsResults reveal that mimicking desire and goal relevance influence restaurant visit intention; however, mimicking desire has a stronger influence than goal relevance. Little difference was found between the tourist and the resident groups in the proposed relationships, except that vicarious expression positively influences mimicking desire in the tourist group but not in the resident group.Practical implicationsThis study guides restauranteurs and social media influencers (foodstagrammers). It shows that consumers value the textual content and aesthetic appeal of photos over the popularity of a post. It also indicates that vicarious expression is more important for tourists than for residents.Originality/valueThis research advances social media marketing literature by proposing a new information processing framework. To the best of the authors’ knowledge, this study is one of the first studies to explore the impact of visual post attributes on individual decision-making behaviours through socially acceptable norms.

Neuromechanical proficiency in elite performance decision-making: An event-related potential (ERP) analysis

This research focuses intensively on the neural mechanisms of action selection responses in elite athletes during high-stakes decision-making. It emphasizes the neuromechanical dimensions of these processes, contrasting the responses of expert and novice players within a theoretical framework of neural information processing in high-performance contexts. Utilizing advanced EEG technologies, including event-related potential (ERP) analysis, the study captures a comprehensive view of both behavioral and neurophysiological data. The central aim is to unravel the intricate neural underpinnings that distinguish elite athletes in their decision-making strategies. Key findings highlight: (1) Enhanced accuracy and swifter reaction times in elite athletes during the action selection phase; (2) Significant neurophysiological differences, marked by pronounced N1 peak amplitudes with prolonged latencies, reduced P2 peak amplitudes with stable latencies, decreased P3 peak amplitudes with reduced latencies, and increased average PSW amplitudes. These discoveries significantly advance our understanding of the neural foundations of expert decision-making in high-performance sports. This study not only sheds light on the cognitive and neural dynamics of elite sports performance but also provides a foundation for developing training and performance enhancement techniques in various high-stakes domains.

Examining presence in immersive virtual reality and website interfaces through the cognitive fit and cognitive load theories

PurposeThe purpose of this paper is to assess the effects of presence on users’ information processing in mediated visualizations using immersive virtual reality (VR) and websites, focusing on information understanding and cognitive load. The impact of user interfaces on users’ perception of presence is also tested.Design/methodology/approachA between-subjects laboratory experiment using two user interfaces – immersive VR and website – is carried out. It is complemented by a self-administered survey on the participants.FindingsThe current results indicate that an immersive VR interface improves tourists’ information processing by producing a heightened sense of presence. The results confirm that presence facilitates tourists’ information processing by increasing information understanding and decreasing cognitive load. Finally, a negative relationship between cognitive load and information understanding is obtained.Practical implicationsVR user interfaces are not only visually appealing but also cognitively optimized. Immersive VR devices can be introduced in the specific sale process of cruise trips through both offline channels (physical stores and travel agencies) and online channels to facilitate tourists’ information processing during the prepurchase phase.Originality/valueThe primary contribution lies in the integration of the theory of presence with two influential information processing frameworks, namely, cognitive load theory and cognitive fit theory. This integration provides a holistic perspective on how user interfaces influence consumer information processing in the unique context of cruise tourism, particularly during the stage of a prepurchase information search.

Normative, expressive, and personal factors associated with cooperation with police: Findings from a longitudinal cohort study

PurposeWhile there has been extensive research on the normative, instrumental, and social explanations for cooperation with police, fewer studies have examined how personal, social-cognitive characteristics might influence willingness to cooperate. This paper integrates models of cooperation with the social information processing framework to understand when individuals are more willing to cooperate with the police. MethodsWe use two waves of data from the Zurich Project on Social Development from Childhood to Adulthood, an ongoing longitudinal study of an ethnically diverse sample of young people from Zurich, Switzerland. ResultsThe results show that, aside from perceptions of police legitimacy, moral neutralization and empathy were directly associated with willingness to cooperate. Police legitimacy moderated the relationship between moral neutralization and cooperation, however this effect was not robust across models. In addition, we found that those who have engaged in serious criminal behavior were less likely to cooperate. ConclusionsWe argue that our findings demonstrate the need for cooperation with police research to adopt a decision-making framework in explaining the decision to indirectly intervene and cooperate. Namely, individuals must first recognize and acknowledge the harm or illegal behavior done when deciding to take action to cooperate with the police.

Puzzle Pictures: A Study on Reversible Figures in Advertising

Reversible figures have fascinated researchers in the psychological sciences since the 1800s, and these investigators have generated a massive amount of information on the psychological underpinnings and manifestations of this intriguing visual phenomenon. However, although these puzzle pictures have consistently appeared in marketing communications, advertising research has been negligent in acknowledging reversible figures as a distinctive form of advertising. This paper aims to fill this research gap by systematically investigating reversible figure ads and studying their effects on variables pertinent to marketing and advertising, thereby contributing to theory and practice. The results obtained from four studies reveal that reversible figure ads boost mainstream indicators of ad effectiveness such as brand attitude, perceived brand innovativeness, and perceived ad creativity. Further, informed by MacInnis and Jaworski’s (1989) information processing framework, this paper supports a novel ad processing model in which reversible figure ads enhance perceptions of ad creativity, increasing perceived brand innovativeness and thereby strengthening brand attitude. Last, the results indicate conditional indirect effects, revealing that this serial mechanism materializes in individuals with higher (vs. lower) levels of artistic interest.

Thermally-robust spatiotemporal parallel reservoir computing by frequency filtering in frustrated magnets

Physical reservoir computing is a framework for brain-inspired information processing that utilizes nonlinear and high-dimensional dynamics in non-von-Neumann systems. In recent years, spintronic devices have been proposed for use as physical reservoirs, but their practical application remains a major challenge, mainly because thermal noise prevents them from retaining short-term memory, the essence of neuromorphic computing. Here, we propose a framework for spintronic physical reservoirs that exploits frequency domain dynamics in interacting spins. Through the effective use of frequency filters, we demonstrate, for a model of frustrated magnets, both robustness to thermal fluctuations and feasibility of frequency division multiplexing. This scheme can be coupled with parallelization in spatial domain even down to the level of a single spin, yielding a vast number of spatiotemporal computational units. Furthermore, the nonlinearity via the exchange interaction allows information processing among different frequency threads. Our findings establish a design principle for high-performance spintronic reservoirs with the potential for highly integrated devices.

How Does Carbon Footprint Information Affect Consumer Choice? A Field Experiment

ABSTRACTThis paper reports the results of a field experiment investigating how attributes of carbon footprint information affect consumer choice in a large dining facility. Our hypotheses and research methods were preregistered via the Journal of Accounting Research’s registration‐based editorial process. Manipulating the measurement units and visualizations of carbon footprint information on food labels, we quantify effects on consumers’ food choices. Treated consumers choose less carbon‐intensive dishes, reducing their food‐related carbon footprint by up to 9.2%, depending on the treatment. Effects are strongest for carbon footprint information expressed in monetary units (“environmental costs”) and color‐coded in the familiar traffic‐light scheme. A postexperimental survey shows that these effects obtain although few respondents self‐report concern for the environmental footprint of their meal choices. Our study contributes to the accounting literature by using an information‐processing framework to shed light on the information usage and decision‐making processes of an increasingly important user group of accounting information: consumers.

Machine Learning Based Fluid-Transportation Monitoring and Controlling

The discipline of fluid mechanics is developing quickly, propelled by previously unheard-of data volumes from experiments, field measurements, and expansive simulations at various spatiotemporal scales. The field of machine learning (ML) provides a plethora of methods for gleaning insights from data that can be used to inform our understanding of the fluid dynamics at play. As an added bonus, ML algorithms can be used to automate duties associated with flow control and optimization, while also enhancing domain expertise. This article provides a review of the background, current state, and potential future applications of ML in fluid mechanics. We provide an introduction to the most fundamental ML approaches and describe their applications to the study, modelling, optimization, and management of fluid flows. From the standpoint of scientific inquiry, which treats data as an integral aspect of modelling, experiments, and simulations, the benefits and drawbacks of these approaches are discussed. Since ML provides a robust information-processing framework, it can supplement and potentially revolutionize conventional approaches to fluid mechanics study and industrial applications.

How risk messages influence tourist processing and sharing: The role of emojis

Understanding how tourists process and share risk messages during a crisis is critical for tourism risk communication. This research develops and tests a theoretical framework of tourist risk information processing and sharing in the context COVID-19 pandemic. A mixed-method design was employed, consisting of three separate studies conducted in different case contexts, involving different experimental subjects. The results showed that: a) risk message framing significantly impacted information sharing; b) heuristic and systematic processing and perceived value of information sharing were critical mediators that underpin the tourists' role shift from “receiver” to “sharer”; c) emojis strengthened tourists’ processing of risk message, and improved their perceived value and information sharing in the risk amplifying frame, compared with the risk attenuating frame. This research advances the risk communication literature and provides implications for destinations to set effective risk communication strategies in times of crisis.

Quantum walks on random lattices: Diffusion, localization, and the absence of parametric quantum speedup

Discrete-time quantum walks, quantum generalizations of classical random walks, provide a framework for quantum information processing, quantum algorithms and quantum simulation of condensed matter systems. The key property of quantum walks, which lies at the heart of their quantum information applications, is the possibility for a parametric quantum speed-up in propagation compared to classical random walks. In this work we study propagation of quantum walks on percolation-generated two-dimensional random lattices. In large-scale simulations of topological and trivial split-step walks, we identify distinct pre-diffusive and diffusive behaviors at different time scales. Importantly, we show that even arbitrarily weak concentrations of randomly removed lattice sites give rise to a complete breakdown of the superdiffusive quantum speed-up, reducing the motion to ordinary diffusion. By increasing the randomness, quantum walks eventually stop spreading due to Anderson localization. Near the localization threshold, we find that the quantum walks become subdiffusive. The fragility of quantum speed-up implies dramatic limitations for quantum information applications of quantum walks on random geometries and graphs.

The Social Context of Mistreatment: An Integrative Review

Management scholars have examined various kinds of workplace mistreatment. These investigations and empirical summaries of the literature show that personal experience of mistreatment at work creates a variety of deleterious problems such as more negative job attitudes, lower performance, and poorer health. The bulk of the literature assumes that the key cause-effect relationship resides at the individual level. However, individuals do not experience mistreatment in a vacuum, but rather in a complex social environment that shapes interpretations of, and reactions to, individual mistreatment. In this integrative review, we depart from prior reviews that focus primarily on personal experiences of mistreatment and instead evaluate the literature on mistreatment in the context of others’ negative experiences. Analyzing 64 empirical articles on own and others’ mistreatment at work, we identify six key theoretical perspectives concerning how others’ experiences shape individual reactions to mistreatment. We then develop a sensemaking and social information processing framework for understanding mistreatment in context. We conclude the article by discussing theoretical and methodological recommendations for future research.

Toward Parsimony in Bias Research: A Proposed Common Framework of Belief-Consistent Information Processing for a Set of Biases

One of the essential insights from psychological research is that people’s information processing is often biased. By now, a number of different biases have been identified and empirically demonstrated. Unfortunately, however, these biases have often been examined in separate lines of research, thereby precluding the recognition of shared principles. Here we argue that several—so far mostly unrelated—biases (e.g., bias blind spot, hostile media bias, egocentric/ethnocentric bias, outcome bias) can be traced back to the combination of a fundamental prior belief and humans’ tendency toward belief-consistent information processing. What varies between different biases is essentially the specific belief that guides information processing. More importantly, we propose that different biases even share the same underlying belief and differ only in the specific outcome of information processing that is assessed (i.e., the dependent variable), thus tapping into different manifestations of the same latent information processing. In other words, we propose for discussion a model that suffices to explain several different biases. We thereby suggest a more parsimonious approach compared with current theoretical explanations of these biases. We also generate novel hypotheses that follow directly from the integrative nature of our perspective.

Intrinsic decoherence effects on correlated coherence and quantum discord in XXZ Heisenberg model

Spin qubits are at the heart of technological advances in quantum processors and offer an excellent framework for quantum information processing. This work characterizes the time evolution of coherence and nonclassical correlations in a two-spin XXZ Heisenberg model, from which a two-qubit system is realized. We study the effects of intrinsic decoherence on coherence (correlated coherence) and nonclassical correlations (quantum discord), taking into consideration the combined impact of an external magnetic field, Dzyaloshinsky–Moriya (DM) and Kaplan–Shekhtman–Entin–Wohlman–Aharony (KSEA) interactions. To fully understand the effects of intrinsic decoherence, two extended Werner-like (EWL) states were considered in this work. The findings indicate that intrinsic decoherence leads to a decay in the quantum coherence and quantum correlations and that their behavior depends strongly on the initial EWL state parameters. Likewise, we found that the robustness of correlated coherence and quantum discord can be controlled through an appropriate choice of the initial state. These findings give us insights into engineering a quantum system to achieve quantum advantages.

Construction of cross-border E-commerce product information traceability model based on blockchain technology

At present, with the emergence of blockchain and the rapid development of the IOT technology, the application of the combination of the two technologies in the field of logistics has become the current trend. This paper analyzes the characteristics of blockchain technology and IOT technology, as well as the advantages of the combination of the two, and on this basis, puts forward the cross-border logistics product information processing framework. In particular, the current cross-border e-commerce products can not be traced. Taking related cases as examples, the cross-border e-commerce product information traceability model is constructed and a case study is conducted on the model.

On the combination of graph data for assessing thin-file borrowers’ creditworthiness

Thin-file borrowers are customers for whom a creditworthiness assessment is uncertain due to their lack of credit history. To address missing credit information, many researchers have used borrowers’ social interactions as an alternative data source. Exploiting social networking data has traditionally been achieved by hand-crafted feature engineering, but lately, graph neural networks have emerged as a promising alternative. Here we introduce an information-processing framework to improve credit scoring models by blending several methods of graph representation learning: feature engineering, graph embeddings, and graph neural networks. In this approach, we aggregate the methods’ outputs to be fed to a gradient boosting classifier to produce a final creditworthiness score. We have validated this framework over a unique multi-source dataset that characterizes the relationships, interactions, and credit history for the entire population of a Latin American country, applying it to credit risk models, application, and behavior. It also allows us to study both individuals and companies. Our results show that the methods of graph representation learning should be used as complements; they should not be seen as self-sufficient methods, as it is currently done. We improve the creditworthiness assessment performance in terms of the measures of Area Under the ROC Curve (AUC) and Kolmogorov–Smirnov (KS), outperforming traditional methods of exploiting social interaction data. In the area of corporate lending, where the potential gain is much higher, our results confirm that the evaluation of a thin-file company cannot solely consider the company’s own characteristics. The business ecosystem in which these companies interact with their owners, suppliers, customers, and other companies provides novel knowledge that enables financial institutions to enhance their creditworthiness assessment. Our results let us know when and on which population to use graph data and the expected effects on performance. They also show the enormous value of graph data on the credit scoring problem for thin-file borrowers, mainly to help companies with thin or no credit history to enter the financial system.

The otolith vermis: A systems neuroscience theory of the Nodulus and Uvula.

The Nodulus and Uvula (NU) (lobules X and IX of the cerebellar vermis) form a prominent center of vestibular information processing. Over decades, fundamental and clinical research on the NU has uncovered many aspects of its function. Those include the resolution of a sensory ambiguity inherent to inertial sensors in the inner ear, the otolith organs; the use of gravity signals to sense head rotations; and the differential processing of self-generated and externally imposed head motion. Here, I review these works in the context of a theoretical framework of information processing called the internal model hypothesis. I propose that the NU implements a forward internal model to predict the activation of the otoliths, and outputs sensory predictions errors to correct internal estimates of self-motion or to drive learning. I show that a Kalman filter based on this framework accounts for various functions of the NU, neurophysiological findings, as well as the clinical consequences of NU lesions. This highlights the role of the NU in processing information from the otoliths and supports its denomination as the “otolith” vermis.

Consumer financial information processing: An integrated approach to examine individual differences

AbstractThis study applied an integrated approach to construct a hierarchical framework for consumer financial information processing. Individual differences in customers' psychological, cognitive and motivational characteristics were examined as antecedents. Using data collected from a sample of 613 U.S. adults, this study used the structural equation modeling method to identify significant direct and indirect relationships amongst personality traits, financial self‐efficacy, financial information‐gathering ability, search motivation, financial information sufficiency and the utilization of heuristics and systematic financial information‐processing modes. The results showed that consumers' information sufficiency (+) directly explained the application of the heuristic mode, whereas consumers' information gathering ability (+), motivation to search (+) and information sufficiency (+) directly explained the use of the systematic mode. Financial self‐efficacy demonstrated positive indirect associations with both heuristic and systematic modes. The results also revealed that personality traits including agreeableness, emotional stability and extraversion indirectly explained the use of information‐processing modes. Agreeableness (−) and emotional stability (+) were indirectly associated with both heuristic and systematic information processing modes, whilst extraversion (+) was only indirectly associated with the heuristic mode. Openness (+) was the only trait that was directly associated with the systematic mode. This study contributes to the literature on consumer financial information processing by connecting individuals' psychological, cognitive and motivational characteristics. The findings of this study provide valuable insights and policy and practical implications within the industry.

Psychographic segmentation to identify higher-risk teen peer crowds for health communications: Validation of Virginia's Mindset Lens Survey.

Audience segmentation is necessary in health communications to ensure equitable resource distribution. Peer crowds, which are macro-level teen subcultures, are effective psychographic segments for health communications because each crowd has unique mindsets, values, norms, and health behavior profiles. These mindsets affect behaviors, and can be used to develop targeted health communication campaigns to reach those in greatest need. Though peer crowd research is plentiful, no existing peer crowd measurement tool has been formally validated. As such, we developed and validated Virginia's Mindset Lens Survey (V-MLS), a mindset-based teen peer crowd segmentation survey to support health communication efforts. Using an online convenience sample of teens (N = 1,113), we assessed convergent and discriminant validity by comparing the V-MLS against an existing, widely-used peer crowd survey (I-Base Survey®) utilizing a multi-trait multi-method matrix. We also examined the V-MLS's predictive ability through a series of regressions using peer crowd scores to predict behaviors, experiences, and traits relevant to health communication campaign planning. The V-MLS demonstrated reliability and convergent and discriminant validity. Additionally, the V-MLS effectively distinguished teen peer crowds with unique health behaviors, experiences, and personal traits. When combined with appropriate information processing and campaign development frameworks, this new tool can complement existing instruments to inform message framing, tone, and style for campaigns that target at-risk teens to increase campaign equity and reach.

Information Processing Capacity of a Single-Node Reservoir Computer: An Experimental Evaluation.

Physical dynamical systems are able to process information in a nontrivial manner. The machine learning paradigm of reservoir computing (RC) provides a suitable framework for information processing in (analog) dynamical systems. The potential of dynamical systems for RC can be quantitatively characterized by the information processing capacity (IPC) measure. Here, we evaluate the IPC measure of a reservoir computer based on a single-analog nonlinear node coupled with delay. We link the extracted IPC measures to the dynamical regime of the reservoir, reporting an experimentally measured nonlinear memory of up to seventh order. In addition, we find a nonhomogeneous distribution of the linear and nonlinear contributions to the IPC as a function of the system operating conditions. Finally, we unveil the role of noise in the IPC of the analog implementation by performing ad hoc numerical simulations. In this manner, we identify the so-called edge of stability as being the most promising operating condition of the experimental implementation for RC purposes in terms of computational power and noise robustness. Similarly, a strong input drive is shown to have beneficial properties, albeit with a reduced memory depth.

Analysis of Text Data For Stock Prediction

Abstract: Accounting for price fluctuations and understanding people's emotions can help to improve stock price forecasting. Only a few models can decipher financial jargon and have stock price change datasets that have been labelled. In this project, we used text mining techniques to extract high-quality data from news and tweets published by legitimate businesses on the internet, allowing us to analyse, decide, and update our database for future use. In this paper, we propose an information gathering and processing framework that combines a natural language processing tool with our algorithms. We use natural language processing and machine learning techniques to make predictions. The result demonstrates the algorithm's ability to foresee favorable outcomes.