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The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

Strategy scholars have widely recognized the central role that narratives play in the construction of organizational identities. Moreover, storytelling is an important strategic asset that firms can leverage to inspire employees, excite investors and engage customers' attention. This chapter illustrates how advancements in computational linguistic may offer opportunities to analyze the stylistic elements that make a story more convincing. Specifically, we use a topic model to examine how narrative conventionality influences the performance of 78,758 craftsmen selling their handmade items in the digital marketplace of Etsy. Our findings provide empirical evidence that effective narratives display enough conventional features to align with audience expectations, yet preserve some uniqueness to pique audience interest. By elucidating our approach, we hope to stimulate further research at the interface of style, language and strategy.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

Touch plays an important role in the evaluations of products. However, prior quantitative touch research does not present a holistic perspective of haptics. This study aims to provide a more comprehensive understanding of haptic information that consumers attain from touch.

Adopting a qualitative research approach, semi-structured, depth interviews were conducted with industry experts.

Based on the findings, this research proposes a conceptual model of consumer haptic perception consisting of seven key influences, namely, the effects of haptic sensation, haptic perception, the influence of individual factors, the influence of external environmental factors, the multi-dimensionality of haptics, haptic cue congruity and haptic dominance.

This study suggests firms to understand the importance of consumers’ haptic perception as an opportunity to engage an individual’s heart and mind through information attained through touch.

This is the first study in marketing to propose a conceptual model of haptics comprising several new haptic influences. This model contributes to the emerging literature of sensory marketing by providing directions for future touch research by capturing the dynamic nature of haptics.

El tacto desempeña un papel importante en nuestras evaluaciones de los productos. Sin embargo, las investigaciones cuantitativas anteriores sobre el tacto no presentan una perspectiva holística de la percepción táctil. Este artículo pretende ofrecer una comprensión más completa de la información táctil que los consumidores obtienen del contacto.

Adoptando un enfoque de investigación cualitativa, se realizaron entrevistas semiestructuradas en profundidad con expertos del sector.

A partir de los resultados, esta investigación propone un modelo conceptual de la percepción táctil del consumidor que consta de siete influencias clave: Los efectos de la sensación háptica, la percepción háptica, la influencia de los factores individuales, la influencia de los factores ambientales externos, la multidimensionalidad de la sensación táctil, la congruencia de las pistas táctiles y la dominancia de la sensación táctil.

Este estudio sugiere a las empresas que comprendan la importancia de la percepción táctil de los consumidores como una oportunidad para captar el corazón y la mente del individuo a través de la información obtenida mediante el tacto.

Este es el primer estudio en marketing que propone un modelo conceptual de la percepción táctil que incluye varias influencias táctiles nuevas. Este modelo contribuye a la literatura emergente del marketing sensorial proporcionando direcciones para la futura investigación táctil al capturar la naturaleza dinámica del sentido del tacto.

触摸在我们对产品的评价中起着重要作用。然而, 在先前定量的触觉研究中并未呈现触觉的整体视角。本文旨在更全面地了解消费者通过触摸获得的触觉信息。

本文采用定性研究方法, 与行业专家进行了半结构化的深度访谈。

本研究提出了一个消费者触觉感知的概念模型, 其中包括七个关键影响因素：触觉的影响、触觉感知、个人因素的影响、外部环境因素的影响、触觉的多维性、触觉线索的一致性和触觉的主导性。

这项研究建议企业了解消费者触觉感知的重要性, 并将触觉信息传达作为吸引消费者心灵的机会。

这是营销领域首次提出触觉概念模型的研究, 该模型包含几种新的触觉影响因素。这个触觉概念模型通过捕捉触觉的动态特性为未来的触觉研究提供方向, 从而为新兴的感官营销文献做出了贡献。