Ways to Measure Loyalty of Service Users

The focus of this study was set on the empirical determination of the differences between the methods of measuring the loyalty of users of services. The research reviewed approaches to evaluating the loyalty of taxi service users in Ho Chi Minh (Vietnam), Moscow (Russia), and Novosibirsk (Russia). The goal implied the segmentation of service users depending on the patterns of conduct and giving advice on the development of loyalty programs. The novelty lies in grouping respondents by age and gender and determining the ways to measure their loyalty. The research findings imply that people under 30 tend to make a choice based on reviews of others. The study developed possible customer behavior models and options for actions depending on their satisfaction and loyalty. This matrix can be used for building a comprehensive strategy to increase the loyalty of their clients. Practical value of the study lies in the possibility of using the data obtained to model the behavior of consumers of different service types based on the proposed loyalty matrix.

General Modeling Method of Threshold-Type Multivalued Memristor and Its Application in Digital Logic Circuits

This paper presents a general modeling method for threshold-type multivalued memristors. Through this memristor modeling method, it is very simple to establish threshold-type memristor behavior models with different numbers of memristance elements, and these models are verified by numerical MATLAB simulations. A corresponding circuit-level SPICE model of the ternary memristor behavior model is developed and simulated in LTspice, shown to be consistent with the MATLAB results. Finally, the SPICE model is used to design the AND gate, OR gate, and three NOT gates of ternary state-based logic, and the effectiveness of the circuit is proved by LTSpice simulation.

Can we use anti-predator behavior theory to predict wildlife responses to high-speed vehicles?

Animals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle. We addressed this gap by (a) providing an overview of the main hypothesis and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generate quantitative predictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed via a simulation approach where we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. We used literature on birds for goals (b) and (c). We considered the following eight models: the economic escape model, Blumstein's economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generate quantitative predictions about escape distances with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape. We derive some applied applications of our finding by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high-speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will be facing in the next decade.

Behavior models of employees and their increasing role in the theory and practice of management

The authors study two basic approaches to modeling human behavior that have a long history. The first one is connected with shaping desired behavior models. Such models can be found in sacred books of many religions. The second approach is connected with characterizing behavior by means of a certain associative image. The authors present a comparative characteristic of both approaches and reveal their methodological difference. The article describes the problems that arise while using associative models of employees’ behavior in a modern organization. At the same time the authors point out the increasing for many countries significance of the approach connected with shaping desired employees’ behavior models. So they use system basis to structure the basic tasks of shaping desired employees’ behavior models. It helps an organization to find a necessary number of employees’ behavior models. They will facilitate achieving the organization’s objectives, solving its tasks, effective exploitation of the resources and achieving the results. The attention is mainly paid to minimization of the increasing threats of the external environment, particularly to neutralization of the influence of those organizations and individuals whose values do not meet the interests of a certain society and business entities. The authors reveal strategic and current relevance of shaping desired employees’ behavior models for organizations and state, and suggest the way to solve this task. The central place is given to government agencies and the leader’s personal example. The article contains a list of major works to be done within a national project on neutralization of the increasing negative influence of the external environment. There is also a list of conditions for harmonic combination of both approaches to modeling employees’ behavior in practice to obtain additional managerial effect. The authors insist that government regulation of shaping desired employees’ behavior models in organizations is inevitable historically. Delays will only increase the lost profit and the costs for compensating the growing damage.

Theoretical Exploration of Consumer Behavior

This article discusses consumer behavior phenomena, its essence and several models and theories. The purpose of the study is to determine consumer behavior patterns, types, necessity and application. In this article we determined a several definitions of consumer behavior. We also studied different theories and models of consumer behavior. The main research question is the following: What are the phenomena of consumer behavior and what kinds of consumer behavior models exist? All data has been collected from the secondary sources, including websites, books, articles, journals and scientific articles. As a result of this work we determined a several definitions of consumer behavior and comparison of them. In this work we also described models and theories of consumer behavior, determined by different authors. Current knowledge will be implemented in a future works, devoted to the identification of consumer behavior models in e-commerce. In the end of this work, we will conclude that consumer behavior analysis can positively effect on company’s revenue.

Terra incognita of vitality problem: Open issues. Part 2

The relevance of studying resilience is among other things conditioned by faster evolution of social objects, increase in general uncertainty, lack of stability, complexity and ambiguity of the dynamics of their state, which, in their turn, lead to an increase in the requirements for adaptation mechanisms of an individual and social groups (families, collectives, sports teams, managerial or project teams); the importance of an individual’s socio-psychological and psychological resources is increasing. Not all spontaneously formed adaptation mechanisms of individual and group subjects are developed in a timely manner, correspond to life situations or change optimally in accordance with changes in the purposes and requirements of the environment. The purpose of the study is to investigate human resilience under conditions of uncertainty; the subjects of the study are social and psychological mechanisms and resources of human resilience; the research methods are historical and theoretical analysis and analysis of the empirical research results. The hypothesis is that maintaining resilience of an individual and social groups through the development of prosocial behavior mechanisms according to the specific model (prototype) is the first stage. Subsequent stages of resilience development presuppose formation of adaptation mechanisms initiated and supported by the evolution of the subject’s “internal conditions”. It is stated that the two states of a person are considered within the boundaries of historically formed paradigms: “below the norm” and “within the norm” of social, psychological and physical functioning. Its characteristics are described by such concepts as “improvement”, “positive adaptation”, “preservation”, “optimal state”, etc. Resilience is viewed as the person’s achievement and maintenance of “social homeostasis” with an orientation towards pro-social behavior models of others and reproduction of such models. Individual’s advancement to a higher level of social functioning and his/her positive professional evolution are often associated with repeated changes and complications of the social conditions of social environment (medium), with the growth in its “resistance”; they remain understudied and retain a kind of “terra incognita” status. One of the possible and probable approaches that contribute to overcoming the binary thinking of the historically established paradigm can be the use of a new methodology, i.e. construction of system triads (that recreate social objects’ integrity) and linear triads that restore the sequences of social objects’ syntheses when they are integrated into systems of a greater generality.

A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning

Inattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using neuroimaging features. However, the performance of inattention estimation could be improved for conventional brain-behavior models with additional feature selection, machine learning algorithms, and validation procedures. This paper aimed to propose a unified framework for inattention estimation from resting state fMRI to improve the classical brain-behavior models. Phase synchrony was derived as raw features, which were selected with minimum-redundancy maximum-relevancy (mRMR) method. Six machine learning algorithms were applied as regression methods. 100 runs of 10-fold cross-validations were performed on the ADHD-200 datasets. The relevance vector machines (RVMs) based on the mRMR features for the brain-behavior models significantly improve the performance of inattention estimation. The mRMR-RVM models could achieve a total accuracy of 0.53. Furthermore, predictive patterns for inattention were discovered by the mRMR technique. We found that the bilateral subcortical-cerebellum networks exhibited the most predictive phase synchrony patterns for inattention. Together, an optimized strategy named mRMR-RVM for brain-behavior models was found for inattention estimation. The predictive patterns might help better understand the phase synchrony mechanisms for inattention.

Towards a Conceptual Framework of Comprehensive Video Game Player Profiles: Player Models, Mental Models, and Behavior Models

Current approaches to player profiling are limited in that they typically employ only a single one of numerous of available techniques shown to have utility for categorizing and explaining player behavior. We propose a more comprehensive Video Game Player Profile Framework that considers the demographic, psychographic, mental model, and behavioral modeling approaches shown to be effective for describing gamer populations. We suggest that our proposed approach can improve the efficacy of video game player profiles by grounding data-driven techniques in game analytics with the theoretical backing of demographic, psychometric, and psychographic measurements. We provide an overview of our proposed framework, discuss the usage and relevance of each component technique, and provide a proof-of-concept demonstration with archived data.

A Discovery Method for Hierarchical Software Execution Behavior Models Based on Components

Software is rapidly evolving and operates in a changing environment; therefore, in addition to software design and testing, it is essential to observe and understand the software execution behavior by modeling data recorded during the execution of the software to improve its reliability. The nested call relationship between methods during the execution of software is common, but most process-mining methods are unable to discover them, only generating flat models with low fitness. Meanwhile, it is easy to generate “spaghetti-like” models with low comprehensibility when dealing with complex software execution data. This paper proposes a component-based hierarchical software behavior model discovery method that can discover hierarchical nested call structures during software runtime, improving the fitness of the model; meanwhile, the proposed method partitions the discovery model into several parts by component information to improve the comprehensibility of the model, which can also reflect the interaction behavior within and between components. The proposed approach was implemented in a process mining toolkit. Using real-life software event logs and public datasets, we demonstrated that compared with other advanced process mining techniques, our approach can visualize actual software execution behavior in a more accurate and easy-to-understand way while balancing time performance.