Behavioral Preferences Research Articles

Predicting Chinese tourists’ B&B preferences through a method of online reviews causality analytic

With the enormous numbers of online reviews posted daily, automatically acquired and analyzed the information that rich of causal logics is of great significance for Chinese B&B enterprises to make accurate business decisions. This study proposed a method based on Chinese NLP technologies to reveal and describe the formation process of tourists' behavioral preferences better than the previous entity relationship extraction and analysis methods. Firstly, Cause-effect syntactic and template pattern had been applied to extraction event casual relations in the online reviews texts. Then, Doc2vec-Kmeans clustering algorithm and key-phrases sorting algorithm were used to generalize data. Finally, causality graph was constructed for visual analysis. To evaluate the performance of the proposed approach, we collected 549, 732 Chinese B&B reviews from Ctrip.com and Qunaer.com. The survey identified 7 common preferences that travelers' concern about nowadays and reveals the reasons behind each of them, as well as concluded that the ‘services’ and ‘conveniences’ of Chinese B&Bs are the trends of travelers’ preferences in the future selections. The results indicated that the proposed approach outperforms the other entity relation extraction method in event forecasting, and most of the subjects believed that the proposed approach can provide more comprehensive and logical traveler's potential demand information.

University commuting during the COVID-19 pandemic: Changes in travel behaviour and mode preferences

One prominent change induced by the COVID-19 pandemic concerns the worldwide use of public transportation for commuting purposes. This study focused on university commuting in Italy by examining the propensity to change transport modes under different infection risk scenarios. Data were collected in 2020 through an online survey of college mobility conducted by the Italian University Network for Sustainable Development. Asking the respondents to consider both a pessimistic and an optimistic scenario, with respect to the risk odds of being infected, we followed a two-step approach to study the prospective travel habits of college users. First, we tested a logit model to estimate the propensity to abandon one's pre-COVID-19 commuting mode. Then, we investigated the factors influencing the choice of switching from public transportation to either cars or active modes by estimating a multinomial logit model. By exploiting the novelty of considering two risk scenarios, this study highlighted that, especially in the pessimistic case, the change to active modes was constrained by spatial aspects in favour of motorized vehicles. From a policy perspective, this COVID-19-based natural experiment advocates transportation authorities taking effective actions to ensure that, in case of emergencies, a modal shift would not benefit more-polluting transport means.

Balancing risk-return decisions by manipulating the mesofrontal circuits in primates.

Decision-making is always coupled with some level of risk, with more pathological forms of risk-taking decisions manifesting as gambling disorders. In macaque monkeys trained in a high risk-high return (HH) versus low risk-low return (LL) choice task, we found that the reversible pharmacological inactivation of ventral Brodmann area 6 (area 6V) impaired the risk dependency of decision-making. Selective optogenetic activation of the mesofrontal pathway from the ventral tegmental area (VTA) to the ventral aspect of 6V resulted in stronger preference for HH, whereas activation of the pathway from the VTA to the dorsal aspect of 6V led to LL preference. Finally, computational decoding captured the modulations of behavioral preference. Our results suggest that VTA inputs to area 6V determine the decision balance between HH and LL.

Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data

Smart cities leverage advanced technologies to enhance urban living through the real-time collection, processing, and analysis of contextual information. The potential to improve residents’ outdoor experiences in these cities increases dramatically as smart technologies are integrated into urban environments, making them more interconnected. It helps to explore the pivotal role of real-time data in optimizing various aspects of city management, focusing on key domains such as traffic, public transportation, emergency response, waste management, and environmental monitoring. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. This model addresses the dynamic and context-aware nature of Smart Cities by proposing a novel framework for real-time contextual information prediction and personalized outdoor activity suggestions for users. Leveraging the vast amount of data generated by Smart City infrastructure, this study integrates advanced data analysis techniques with deep learning models to enhance the urban living experience. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. The framework begins by collecting and processing diverse datasets from sensors, Internet of Things (IoT) devices, and other urban sources to create a comprehensive understanding of the current city context. Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are employed to analyze this data and predict real-time contextual information, including weather conditions, traffic patterns, and social events. It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. The result shows that the proposed air pollution model predicted 96.06700 PM2.5 concentration levels, subsequently the temperature model predicted 14.06800∘C. The integration of real-time contextual information prediction and personalized outdoor activity suggestions showcases the potential for creating more engaging and user-centric Smart City ecosystems. This research attempts to provide personalized recommendations that are in line with users’ preferences, the state of the environment at the time, and other pertinent contextual factors by utilizing data from multiple sources, including IoT devices, mobile applications, and environmental sensors.

Research on Personalized Marketing Strategy of Financial Products Based on Big Data

With the rapid development of information technology, big data has become one of the most important tools for personalized marketing in the financial industry. Based on big data analysis, this study investigates the practice and impact of personalized marketing strategies for financial products in the current economic environment. Financial institutions can create customer portraits and tailored products and services by gathering and studying data on consumers' buying patterns, behavioral preferences, social circles, and interactive feedback. Using various methods, including case studies, in-depth interviews, and quantitative analysis, we took a closer look at how big data can help financial institutions improve marketing efficiency, optimize product design, increase customer satisfaction, and improve customer loyalty. Research shows that big data can greatly support the marketing of financial products, improving market segmentation, providing better opportunities for cross-selling and value-added services, and optimizing marketing resources through predictive analytics, instant marketing, and highly personalized customer experiences. Nonetheless, financial institutions must demonstrate effective skills in managing data, using analysis technology, protecting privacy, and implementing personalized marketing strategies to fully benefit from these advantages.

Classification of User Preference for Self-Driving Mode and Behaviors of Autonomous Vehicle

Classification of User Preference for Self-Driving Mode and Behaviors of Autonomous Vehicle

The Predictive Power of Preferences for Sustainable Consumption Behavior

The Predictive Power of Preferences for Sustainable Consumption Behavior

СОВРЕМЕННЫЕ ТЕНДЕНЦИИ ПРИМЕНЕНИЯ ИННОВАЦИОННЫХ ТЕХНОЛОГИЙ В БАНКОВСКОМ БИЗНЕСЕ

The paper presents an analysis of the current trends in the development of banking activities and modern practice of innovative technologies application in the banking business. It is defined that nowadays the main consumers of banking services are representatives of the millennial generation, whose behavioral characteristics and preferences should be taken into account in practice. Authors show how innovative financial technologies influence the methods and approaches in the work of national banking organizations. Modern financial innovations are considered, which are gradually being tested, implemented and offered to clients by various Russian banks. It is emphasized that digital mortgage is one of the most popular banking products and the possibility of its digital registration has a beneficial effect on the development of that area in banking. Among the potential vectors for further development, an omnichannel approach to interaction with clients is highlighted. It aims to increase the efficiency of communication policy, service speed and the best possible satisfaction of the needs of users of financial products.

Decision-making factors in the childbearing of women working at Birjand University of Medical Sciences during COVID-19 pandemic

Problem consideredThe emergent corona disease has increased the death rate and reduced the birth rate. The researchers aim to examine the decision-making factors in fertility from the point of view of the working women of the … University of Medical Sciences during the Covid-19 pandemic. MethodsIn this descriptive-analytic study, all married women working in the BUMS, as the population of the study, were included. A cluster sampling method was used and 262 people were selected. To collect data, a questionnaire was used concerning the factors influencing fertility decision-making during the Covid-19 pandemic. The content validity and reliability of the questionnaire were confirmed. After gathering questionnaires, data were analyzed using SPSS-19 and descriptive and analytical statistical methods. ResultsFactors affecting fertility decision-making have been presented in 6 areas. The area of knowledge about fertility was at the optimal level and the other areas were at the average level. There was no significant relationship between the variable of knowledge and other factors affecting fertility. Likewise, there was a statistically significant direct relationship between the attitude variable and mental norms (p = 0.009, r = 0.16), perceived behavioral control (p < 0.001, r = 0.416), preferences, and tendencies (p < 0.001, r = 0.557). Besides, a statistically significant relationship existed between attitude and covid worry in the opposite direction (p < 0.001, r = − 0.384). ConclusionThe results of the current study revealed that an attitude is a more important factor than knowledge in determining the childbearing of working women during the COVID-19 pandemic. The current study also implies that interventions to improve the attitude of working women towards childbearing may be more effective than interventions to increase their knowledge.

Elevating Purchase Intentions: The Resonance of Electronic Word of Mouth (eWOM)

This research investigates the influential power of Electronic Word of Mouth (eWOM) in elevating purchase intentions among consumers. In the digital age, eWOM has emerged as a dynamic force shaping consumer behavior and brand preferences. This study aims to unravel the mechanisms through which eWOM resonates with consumers, impacting their intentions to make purchases. The research objectives are meticulously defined, focusing on the identification of key eWOM factors, an examination of their impact on consumer perceptions, and an exploration of how these factors influence purchase intentions. A comprehensive review of existing literature in the field sets the stage for this inquiry. To ensure robust findings, the study employs rigorous participant selection methods, embracing diversity across demographics and consumer segments. Data collection strategies encompass surveys, interviews, and analysis of online interactions, underpinned by ethical considerations such as informed consent and privacy protection. The findings of this research illuminate the significant role of eWOM in shaping consumer perceptions and purchase intentions. It offers valuable insights for businesses, marketers, and digital platforms seeking to harness the potential of eWOM as a strategic tool for brand promotion and customer engagement. By shedding light on the resonance of eWOM in the modern marketplace, this study contributes to the evolving discourse on digital marketing and consumer behavior. It equips businesses with actionable strategies to leverage eWOM effectively, fostering brand loyalty and driving purchase intentions in an increasingly interconnected and digital world.

Physiological condition infers habitat choice in juvenile sockeye salmon.

The amount of time that juvenile salmon remain in an estuary varies among and within populations, with some individuals passing through their estuary in hours while others remain in the estuary for several months. Underlying differences in individual physiological condition, such as body size, stored energy and osmoregulatory function, could drive individual variation in the selection of estuary habitat. Here we investigated the role of variation in physiological condition on the selection of estuarine and ocean habitat by sockeye salmon (Oncorhynchus nerka) smolts intercepted at the initiation of their 650-km downstream migration from Chilko Lake, Fraser River, British Columbia (BC). Behavioural salinity preference experiments were conducted on unfed smolts held in fresh water at three time intervals during their downstream migration period, representing the stage of migration at lake-exit, and the expected timing for estuary-entry and ocean-entry (0, 1 and 3 weeks after lake-exit, respectively). In general, salinity preference behaviour varied across the three time periods consistent with expected transition from river to estuary to ocean. Further, individual physiological condition did influence habitat choice. Smolt condition factor (K) and energy density were positively correlated with salinity preference behaviour in the estuary and ocean outmigration stages, but not at lake-exit. Our results suggest that smolt physiological condition upon reaching the estuary could influence migratory behaviour and habitat selection. This provides evidence on the temporally dependent interplay of physiology, behaviour and migration in wild juvenile Pacific salmon, with juvenile rearing conditions influencing smolt energetic status, which in turn influences habitat choice during downstream migration. The implication for the conservation of migratory species is that the relative importance of stopover habitats may vary as a function of initial condition.

零售商行为偏好下双渠道绿色供应链博弈分析

零售商行为偏好下双渠道绿色供应链博弈分析

The Mediating Effect of Appearance of Appearance in the Relationship between the 20s and Cosmetics Purchase Behavior

This study was attempted to find out demographic characteristics of women in their 20s in China, identify lifestyle, appearance interest, and cosmetics purchase behavior, and reveal the mediating effect of appearance interest in the effect of lifestyle on cosmetics purchase behavior. To this end, this study selected a woman in her 20s in China as the subject of the survey and conducted a survey. A total of 491 copies of the survey were used as final analysis data, excluding 15 incomplete or insincere responses among the questionnaires collected from August 1 to August 31, 2023. The data analysis used frequency analysis, factor analysis, reliability verification, technical statistics analysis, correlation analysis, regression analysis, and Sobel test using the SPSSWIN 21.0 program, and the following analysis results were raised. First, the most common characteristics of the survey subjects were the current residence in large cities (Beijing, Tianjin), the final academic background was a four-year university (re-school), unmarried, employed, monthly income was less than 500,000 won, and the average monthly cosmetics purchase cost was less than 200,000 to 300,000 won. Second, the correlation with each sub-factor of lifestyle, appearance interest, and cosmetics purchasing behavior all showed a positive (+) correlation, indicating that the higher the lifestyle, the more active the appearance interest and the higher the cosmetics purchasing behavior. Third, in terms of the impact of lifestyle on appearance interest, the higher the trend-seeking type and personality-seeking type, the higher the appearance interest. Fourth, the higher the trend-seeking type, a sub-factor of lifestyle, the higher the self-directed type of cosmetics purchase behavior, and the higher the trend-seeking type of lifestyle, the higher the brand preference for cosmetics purchase behavior. Fifth, as a result of verifying the mediating effect of appearance interest in the relationship between lifestyle and cosmetics purchase behavior, the mediating effect of appearance interest in the relationship between fashion-seeking type and self-directed type of cosmetics purchase behavior was significant, and the mediating effect of appearance interest in the relationship between fashion-seeking type and cosmetics purchase behavior was significant. The results of step-by-step verification of the mediating effect of appearance interest in the relationship between lifestyle and cosmetics purchase behavior are as follows. In Stage 1 and Stage 2, the significant trend-seeking type no longer significantly affects the self-directed type of cosmetics purchase behavior in Stage 3, indicating that appearance interest completely mediates the relationship between fashion-seeking type and self-directed type of cosmetics purchase behavior, and appearance interest partially mediates the relationship between fashion-seeking type and brand preference type of cosmetics purchase behavior. The results of this study are expected to be used as basic data necessary to identify the characteristics of the lifestyle of women in their 20s in Huabei, China, and to develop and specify cosmetics industry services and marketing strategies based on lifestyle using the results of this study are expected to be used.

U-SHAPED RELATIONSHIP BETWEEN MANAGERIAL HERD BEHAVIOR AND CORPORATE FINANCIALIZATION WITH THE MODERATING EFFECT OF CORPORATE GOVERNANCE: EVIDENCE FROM CHINA

Based on behavioral finance theory, we discuss the influence of managers’ herd behavior on corporate financialization from the perspective of managers’ behavioral preferences. Empirical testing was conducted using data from nonfinancial listed firms on the Shanghai and Shenzhen A-shares from 2007 to 2021 and a U-shaped relationship was found between managerial herd behavior and corporate financialization. When managerial herd behavior is within an appropriate range, the increase in managerial herd behavior has a negative influence on corporate financialization. In contrast, excessive managerial herd behavior leads to excessive corporate financialization. Additionally, corporate governance has a weakening effect on this relationship. Heterogeneity analyses indicate significant disparities in the effect of managerial herd behavior on corporate financialization among enterprises with diverse ownership structures. Finally, corporate financialization and innovation investments have an inverted U-shaped relationship, and their relationship is moderated positively by management herd behavior. Our results have strong practical significance for fostering the balanced growth of the financial sector and the real economy.

How cognitive selection affects language change

Like biological species, words in language must compete to survive. Previously, it has been shown that language changes in response to cognitive constraints and over time becomes more learnable. Here, we use two complementary research paradigms to demonstrate how the survival of existing word forms can be predicted by psycholinguistic properties that impact language production. In the first study, we analyzed the survival of words in the context of interpersonal communication. We analyzed data from a large-scale serial-reproduction experiment in which stories were passed down along a transmission chain over multiple participants. The results show that words that are acquired earlier in life, more concrete, more arousing, and more emotional are more likely to survive retellings. We reason that the same trend might scale up to language evolution over multiple generations of natural language users. If that is the case, the same set of psycholinguistic properties should also account for the change of word frequency in natural language corpora over historical time. That is what we found in two large historical-language corpora (Study 2): Early acquisition, concreteness, and high arousal all predict increasing word frequency over the past 200 y. However, the two studies diverge with respect to the impact of word valence and word length, which we take up in the discussion. By bridging micro-level behavioral preferences and macro-level language patterns, our investigation sheds light on the cognitive mechanisms underlying word competition.

Recent record of Eurasian Otter Lutra lutra (Linnaeus, 1758) (Mammalia: Carnivora: Mustellidae) from Kerala part of the Western Ghats, India and an insight into the behaviour and habitat preferences

We report a recent record of the elusive Eurasian Otter Lutra lutra from Kerala, through a direct observation in the higher reaches of the Chinnar Wildlife Sanctuary within the Western Ghats, southern India. Field observation involved the sighting of two Eurasian Otters in a rocky-torrential stream in March 2020. The otters displayed a hyperactive foraging behaviour, searching among submerged rocks and crevices, followed by occasional dives. They seemed particularly interested in areas where water rushed with force, avoiding locations with low water flow or shallow pools. The observation site, at 1,275 m altitude in Chinnar Wildlife Sanctuary, located in the Western Ghats of Kerala region, featured wet evergreen vegetation, transitional sholas (stunted evergreen forests), and riparian patches along a stream characterised by rapid water flow, boulders, and fallen trees. This habitat, as observed by others, was also indicative of Asian Small-clawed Otter Aonyx cinereus suitability, suggesting potential resource partitioning between the two otter species. This discovery challenges previous assumptions about the preferred habitat of Eurasian Otters in the Western Ghats and emphasizes the need for comprehensive research on the taxonomy, distribution, population status, and behaviour of these possibly sympatric species of otters, the Asian Small-clawed Otter and the Eurasian Otter. Such studies are crucial for the conservation of both these species, which face threats from habitat loss, sand mining, hunting, and population decline. Preserving and restoring riparian vegetation in the higher reaches of the Western Ghats is essential for their protection within this biodiversity hotspot.

Research on Exhibition Marketing Strategies in the Context of Network Era

Abstract In the network era, consumers’ behavior of shopping through the Internet can be seen everywhere in daily life. In this paper, starting from consumers’ online shopping behavior preference, the equilibrium mode of conventional advertising is studied, based on which the mass and targeted advertising strategies under decentralized and centralized decision-making modes are proposed, respectively. After comparing and studying the marketing channel structure in various modes, the corresponding advertising optimization model was established to derive four theorems. According to the deduced theorems, the enterprise revenue model is constructed and solved to maximize the benefits. Compare the mixed and separated equilibrium models of the two-stage model in the sales process, and at the same time, construct the XGBoost algorithm model to classify the characteristics of consumers’ shopping behaviors and collect the enterprise’s product marketing strategies. The results of the empirical experiments show that the reasons why consumers do not buy the exhibits of Company W are mainly due to “the price is too high”, “the sense of participation is insufficient” and “the attraction is too low”, with the values of 2.5, 2.5, 2.5, 2.5 and 2.5 respectively. The number of consumers who are satisfied and very satisfied with W brand exhibitions is the largest, and the number of people in all four aspects is around 50, so the marketing strategy of W company exhibitions adjusted according to consumer behavioral preferences is effective.

THE RATIONALITY OF URBAN FINANCIAL NETWORK LAYOUT BASED ON POI DATA

Abstract. Financial outlets are branches or business outlets of financial institutions such as banks, investment and finance companies and insurance companies in cities, which are used to provide a variety of financial services and businesses. The location of financial outlets is usually determined on the basis of factors such as population density, commercial areas, residential areas and transportation convenience. As an important carrier of urban financial services, the rationality of the layout of financial outlets can reflect the financial needs and behavioral patterns of people in the city. Urban functional area is a product after urbanization, different from urban land use type which focuses on the classification of urban land use through natural conditions, urban functional area mainly analyzes the internal spatial structure of the city, focusing on the discernment of the dominant functions within the built-up area of the city. By categorizing urban functions such as residence, commercial service and industry, the spatial distribution of urban functions can be grasped to provide support for urban planning and development. Urban functional areas can reveal human behavior patterns, needs and preferences in different areas. The current research on financial outlets mainly focuses on the accessibility and coverage of financial outlets, and researchers treat financial outlets as spatially undifferentiated points, ignoring the problem of spatial heterogeneity of financial outlets within different functional zones. When studying the rationality of the layout of urban financial outlets, considering different functional zones within the city can better meet the financial needs of residents, enterprises and institutions in the region and improve the accessibility and convenience of financial services. Analyzing the comprehensive service capacity of financial outlets within different functional areas is obviously insufficient in the current study, which is not conducive to the configuration planning of financial outlets and the promotion of the accessibility of urban financial services. The wide application of Point of Interest (POI) data in spatial analysis provides a new perspective for the study of urban financial outlets, for this reason, this paper analyzes and researches the current situation and rationality of the spatial layout of financial outlets based on POI data, under the perspective of the functional areas of the city, and combined with the population travel vitality index. The Term Frequency-Inverse Document Frequency (TF-IDF) algorithm is used to process the POI data to generate the urban functional area, on the basis of which the travel vitality index in the city is calculated by combining the population coverage and road network density, so as to judge the reasonableness of the layout of urban financial outlets. This helps us to better understand the behavioral decision-making process and social interaction of human beings about financial life in the city, and provides guidance and decision-making support for urban financial layout planning, financial public services and financial policies.

GTPAN: Global Target Preference Attention Network for session-based recommendation

Session-based recommendations (SBR) aim to predict the user’s next choice of items based on historical data. It is hard to access the interaction information between users and we can only analyze the user’s behavioral preferences based on the current session characteristics, which makes the recommendation task in this field quite challenging. Recently, considerable research has grown up around the theme of SBR. Previous works apply attention mechanisms and Graph Neural Networks (GNN) to achieve remarkable results. However, these methods ignore two main points: one is losing sight of the specific position of items in the session; the other is to drive global preference with local preference without considering the offset of the target preference among items in the session. This paper proposes a Global Target Preference Attention Network (GTPAN) Model with position information for the session-based recommendation. We introduce a position-aware embedding module to eliminate the effect of position missing. Considering that each item in the session is potentially rich in latent information of target preference, we use a target preference attention layer to learn the target preference vector of the session. Extensive experiments on three real datasets show that the proposed GTPAN model outperforms state-of-the-art session-based recommendation methods, especially on datasets with long sessions.

Habitat specialisation and resource availability determine dispersal behaviour in four afro‐tropical butterflies

Abstract Ecological demands, such as sex and environmental conditions, determine species' behaviour, such as dispersal, and shape local population structures. However, only marginal knowledge of these factors exists for afro‐tropical insects so far. Therefore, we performed a mark–release–recapture study in the coastal region of Kenya over a period of 2 months, covering the dry and the rainy seasons. As study species, we used four co‐occurring but ecologically diverging butterfly species, the forest species Bicyclus safitza and Junonia natalica and the savannah species Junonia oenone and Byblia ilithyia. The study area represents an ecosystem mosaic consisting of anthropogenic habitats as well as natural forest and savannah, assessed by detailed landscape mapping. We obtained 1701 capture events, comprising 1224 individuals with an overall recapture rate of 24% (ranging from 15% to 48%). The analyses based on these results allowed an assessment of the population ecology, dispersal behaviour and habitat preferences of these species. Demographic models inferred different patterns of population dynamics among species. All species preferred the habitats they were known for. The observed dispersal distances were rather low ranging from a mean of 25 to 72 m, referring to taxa and sexes. The dispersal extrapolations were unreliably low for the negative exponential function, while the values for inverse power function were credible. The latter predicted a considerably higher long‐distance dispersal capacity in males than in females of the forest species B. safitza and J. natalica, while females dispersed more than males in the savannah species J. oenone and B. ilithyia. Our results obtained from these four ecologically diverging afro‐tropical butterfly species underline that species’ behaviour, such as dispersal, is strongly determined by habitat preferences, sex and the availability of resources.