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This paper aims to introduce a new mixed integer programming formulation and associated heuristic algorithm to solve the Military Nodal Capacity Problem, which is a type of supply chain network design problem that involves determining the amount of capacity expansion required at theater nodes to ensure the on-time delivery of military cargo.

Supply chain network design, mixed integer programs, heuristics and regression are used in this paper.

This work helps analysts at the United States Transportation Command identify what levels of throughput capacities, such as daily processing rates of trucks and railcars, are needed at theater distribution nodes to meet warfighter cargo delivery requirements.

This research assumes all problem data are deterministic, and so it does not capture the variations in cargo requirements, transit times or asset payloads.

This work gives military analysts and decision makers prescriptive details about nodal capacities needed to meet demands. Prior to this work, insights for this type of problem were generated using multiple time-consuming simulations often involving trial-and-error to explore the trade space.

This work merges research of supply chain network design with military theater distribution problems to prescribe the optimal, or near-optimal, throughput capacities at theater nodes. The capacity levels must meet delivery requirements while adhering to constraints on the proportion of cargo transported by mode and the expected payloads for assets.

To provide the results of research to evaluate the usability of a University of Colorado at Boulder Libraries digital initiatives project that provides online access to historical Aerial Photographs of Colorado.

This paper describes usability testing conducted as a part of a user‐centered redesign. The three stages of the evaluation – a requirements analysis, a heuristic evaluation, and user testing – are explained and the findings are discussed.

The usability testing revealed the needs of the project's target user group and identified issues with the interface that will be addressed in its redesign. It has also contributed to the larger understanding of how researchers use digital Aerial Photographs and their preferred methods of access and desired functionalities.

Results from the study will be used to guide the redesign of the Aerial Photographs of Colorado digital library and can be generalized to add to a broader understanding of the usability requirements for a digital library of geospatial materials. Libraries providing a digital collection of geospatial materials may use these findings to inform design decisions.

Much research has been done on the use and evaluation of digital libraries, but few articles have reported on usability studies of online collections of cartographic materials similar in structure to Aerial Photographs of Colorado. It is hoped that these findings will be instructive to librarians designing and evaluating similar digital libraries.

Poses the question: do women have different leadership styles from those of men? With the current and projected impact of women‐led business on the world′s economies, this question is an important one. Surprisingly, however, there is an insignificant amount of empirical research into this leadership‐gender debate – most of the contemporary literature is purely conceptual. Therefore, the goal of the present study is to initiate scientific inquiry of this topic. An exploratory investigation of a sample of women business owners and managers was conducted to examine their leadership styles. From this preliminary study, utilizing the qualitative research methodology of content analysis, a heuristic model of female leadership is developed.

This paper aims to deal with the formulation of the technological principle for precise positioning using global navigation satellite systems (GNSS) in railway engineering during construction and maintenance of a railway line and its spatial position. Solution of optimal route is based on finding the shortest Hamiltonian path in the graph method with additional conditions in nodes.

The core of the algorithm is a dynamic data structure which is based on events list. The optimization of field measurement solves the time demands and brings economic effectiveness.

The technology enables to determine the precise position with absolute difference limit from 10 to 15 mm within GNSS CZEPOS permanent network in the territory of Czech Republic.

Technology is the result of applied research.

This technology innovates the current procedure of geodetic control network determination used by Railway Infrastructure Administration (state organization) in Czech Republic.

The event means measurement at a given track point and time for a specified duration of observation. The algorithm was realized in Borland Delphi. The optimization of field measurement solves its time demands and increases economic effectiveness. The technology enables precise position determination with absolute difference limit from 10 to 15 mm within GNSS CZEPOS permanent network in the territory of Czech Republic. It has been verified in field selected electrified and non-electrified railway lines.

Identifying the fundamental characteristics of meaning and deriving an automated meaning‐analysis procedure for machine intelligence.

Semantic category theory (SCT) is an original testable scientific theory, based on readily available data: not assumptions or axioms. SCT can therefore be refuted by irreconcilable data: not opinion.

Human language involves four totally independent semantic categories (SC), each of which has its own distinctive form of “Truth”. Any sentence that assigns the characteristics of one SC to another SC involves what is termed here “Semantic Intertwine”. Semantic intertwine often lies at the core of semantic ambiguity, sophistry and paradox: problems that have plagued human reason since antiquity.

SCT is applicable to any endeavour involving human language. Research applications are therefore somewhat extensive. For example, identifying metaphors posing as science, or natural language processing/translation, or solving disparate paradox types, as illustrated by worked examples from: The Liar Group, Sorites Inductive, Russell's Set Theoretic and Zeno's Paradoxes.

To interact successfully with human language, behaviour, and belief systems, as well as their own environment, intelligent machines will need to resolve the semantic component/intertwines of any sentence. Semantic category analysis (SCA), derived from SCT, and also described here, can be used to analyse any sentence or argument, however complex.

Both SCT and SCA are original. Whilst “category error” is an intuitive notion, the observably precise nature, number and modes of interaction of such categories have never previously been presented. With SCT/SCA the rigorous analysis of any argument, whether foisted, valid, or obfuscating, is now possible: by man or machine.

In the retailing industry, database is the time and place where a retail transaction is completed. E-business processes are increasingly adopting databases that can obtain in-depth customers and sales knowledge with the big data analysis. The specific big data analysis on a database system allows a retailer designing and implementing business process management (BPM) to maximize profits, minimize costs and satisfy customers on a business model. Thus, the research of big data analysis on the BPM in the retailing is a critical issue. The paper aims to discuss this issue.

This paper develops a database, ER model, and uses cluster analysis, C&R tree and the a priori algorithm as approaches to illustrate big data analysis/data mining results for generating business intelligence and process management, which then obtain customer knowledge from the case firm’s database system.

Big data analysis/data mining results such as customer profiles, product/brand display classifications and product/brand sales associations can be used to propose alternatives to the case firm for store layout and bundling sales business process and management development.

This research paper is an example to develop the BPM of database model and big data/data mining based on insights from big data analysis applications for store layout and bundling sales in the retailing industry.

In terms of service hospitality, recent discussions of value-in-use from the perspective of service-dominant logic have focused on the customer’s determination of value and control of the value creation process. The purpose of this paper is to extend these discussions by exploring the value creation process in the Western-style restaurant in Taiwan, which is developed value-in-eat creation for restaurants. In Taiwan, Western-style restaurants are as popular as Chinese restaurants because of globalization and cultural integration. However, to local restaurateurs and managers, managing a Western-style restaurant in terms of localization and hospitality on value-in-eat creation presents both academic and practical issues. Thus, this paper aims to investigate Western-style restaurant hospitality management alternatives on the value-in-eat creation process in Taiwan using a data mining approach.

Based on a market survey, a total of 1,187 questionnaires was incorporated into a database. The questionnaire design is divided into 7 parts with 35 items. All questions are designed as nominal and ordinal (not the Likert scale) scales. Data mining approach, including cluster analysis and association rules, cluster analysis is investigated possible customer profiles and association rules is implemented to explore customer preference patterns and rules on the value-in-eat creation process.

Data mining results show two patterns including Pattern 1: meal patterns and customer preferences for restaurant hospitality management and Pattern 2: customer relationship management (CRM) for restaurant hospitality management that customer profiles and preferences on meal patterns, service patterns and CRM are engaged to suggest effective Western restaurant hospitality management alternatives, such as proper bundles for restaurant types, meals, exotic atmosphere and services of hospitalities in terms of a value-in-eat creation process.

To the best of the authors’ knowledge, this study is the first study to investigate consumers’ behaviors in Western-style restaurants using the measurement of nominal and ordinal scale for questionnaire development and further to implement a data mining approach on selected data samples. In addition, this study illustrates the patterns/rules of Taiwan customer preferences that best explain the knowledge of how to manage Western-style restaurants from the perspective of customer hospitality using data mining.

在酒店服务领域, 近期的从服务主流逻辑为视角关于使用价值的讨论主要集中在消费者对价值的定义以及掌控价值创造的过程。本研究的主要目的是拓展这些相关的讨论从而发掘关于在台湾经营的西餐厅的顾客价值创造过程, 进而开发餐厅的饮食价值创造。由于全球化进程, 台湾的西餐厅和中餐厅同样受欢迎。然而, 对于本地的餐厅所有人和经营管理者来讲, 管理西餐厅关于价值创造过程中的地方化和服务管理还存在学术和实践问题。因此, 本文运用了数据挖掘的方法对西餐关于价值创造的另类途径进行了探索。

基于市场调研, 本研究导入了1187份问卷作为数据库。问卷由7部分35项条目组成。所有问题以称名量表和顺序量表（非李克特量表）测量。数据挖掘包括了聚类分析和关联分析。聚类分析用来分析消费者概况, 关联分析来探究顾客倾向以及饮食价值创造过程。

数据挖掘结果显示了两种模式, 1：食物以及顾客对餐厅的接待管理的偏爱以及模式, 2：客户关系管理包括顾客概况和对饮食模式的偏爱, 服务模式以及顾客关系维护的另类建议, 诸如适度的捆绑销售包括餐厅种类, 菜系, 异域风情的就餐环境以及服务来体现饮食价值创造的过程。

本研究是首次探索了用称名量表和顺序量来研发的消费者问卷并且运用了数据挖掘的方法研究了西餐厅的消费者行为。 此外, 本研究阐明了台湾消费者的偏爱模式从而更好的解释了如何用数据挖掘的方法来研究西餐厅的服务管理。

Line sticker, a social media, it allows users to exchange multimedia files and engage in one-to-one and one-to-many communication with text, pictures, animation and sound. The purpose of this paper is to examine various Taiwan user experiences in the Line sticker use behaviors. Further, this research looks at how the situations of Line sticker proprietors and their affiliates are disseminated for formulating social media marketing (SMM) in its business model concerns.

This study examines the experience of various Taiwanese Line stickers users utilizing a market survey, a total of 1,164 valid questionnaire data, and the questionnaire is divided into five sections with 30 items in terms of the database design. All questions use nominal and order scales. This study develops a big data analytics approach, including cluster analysis and association rules, based on a big data structure and a relational database.

The authors divide Taiwan Line sticker users into three clusters by their profiles and then find each group’s social media utilization and online purchase behaviors for investigating the Line sticker SMM and business models.

This is the first study to offer a big data analytics to investigate and analyze the varieties in the use of Line sticker by exploring users’ behaviors for further SMM and business model development.

The purpose of this paper is to draw on social exchange theory and heuristic–systematic model to examine how peer-to-peer (P2P) lending firms can enhance their customer acquisition by achieving mobile social media popularity.

Content data collected from multiple sources (websites and mobile applications) were employed to validate the research model.

The mobile social media popularity of P2P lending firms positively influences their customer acquisition. Furthermore, the heuristic cues (i.e. source credibility and content freshness) and the systematic cue (i.e. transaction relevance) potentially affect the firms’ mobile social media popularity.

Mobile social media is not only a platform for firms’ image-building but a critical means of acquiring actual customers. The appropriate use of heuristic–systematic cues in a mobile interface is useful for firms to achieve high user popularity despite the challenges derived from the mobile context.

To achieve higher user popularity in the competitive online world, firms should dedicate greater effort in determining the adequate heuristic–systematic cues designed for the interface of their mobile social media account. The effect of popularity can then help the firms acquire more customers.

This study extends the understanding of social exchange in the context of mobile social media accounts and enriches the knowledge on business value of mobile social media popularity. This paper also contributes to the literature by relating heuristic–systematic cues to firms’ mobile social media popularity.

To describe consumers’ heuristic and analytical searches for a pre‐purchase information acquisition, and to assess the correspondence of flexibility of information task and the information found with a search.

Propositions based on current research in web use and consumer studies. Tracked records of searches are used for descriptive analysis of transitional patterns in the data. Regression is used for statistical verification of the information provided by searches.

Consumer searches center on chaining events, indicating heavy reliance on hyperlink navigation between web sites. Formal searches are seldom used, although when employed, tend to have a high level of diagnosticity. The emphasis on heuristic behavior is logical, as the way consumer information is currently presented on the internet rewards for this type of behavior. Use of heuristic search increases the likelihood of access to flexibly presented information.

Consumers favor heuristic trial‐and‐error searches even in focused fact‐finding search tasks, which are typically considered the domain of analytical seeking. Consumers seem to benefit most from apparently inefficient, reactive and heuristic searches, because these are more likely to provide information in a format that the consumer can adapt. Convenience sample limits generalizability of findings.

While there is an increasing body of knowledge concerning internet use for finding information, fewer studies have focused on consumer uses of the web in search. This paper provides new information of online consumers, an increasingly important topic.