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Internet-based business-to-business electronic procurement (B2B e-procurement) systems are rapidly becoming the primary platform for interorganizational transactions and the delivery of products and services in the travel and tourism industries. Therefore, the purpose of this study is to investigate the critical success factors (CSFs) and implementation strategies for B2B e-procurement systems in travel agency supply chains.

This study developed a multifaceted evaluation framework that draws on the relevant literature and the technology–organization–environment framework. The CSFs underlying B2B e-procurement adoption were identified using hybrid criteria decision-making methods. Purposive sampling was used, and 49 valid questionnaires were obtained from retail travel agencies in Taiwan.

The results reveal that the top four CSFs are system stability, system reliability, sales dynamics and product line availability. By focusing on these CSFs, travel wholesalers can most effectively allocate their limited resources to provide an extensive range of products and services to their clients, improve e-procurement services and enhance interorganizational collaboration in travel agency supply chains.

This study developed a multifaceted evaluation framework and identified four CSFs to assist in the adoption of B2B e-procurement systems in travel agency supply chains.

基于 Internet 的企业对企业电子采购（B2B 电子采购）系统正迅速成为旅行和旅游业中组织间交易以及产品和服务交付的主要平台。 因此, 本研究的目的是调查旅行社供应链中 B2B 电子采购系统的关键成功因素 (CSF) 和实施策略。

本研究开发了一个多方面的评估框架, 该框架借鉴了相关文献和技术-组织-环境框架。 采用混合标准决策 (MCDM) 方法确定了采用 B2B 电子采购的 CSF。 采用有目的的抽样方式, 共从台湾零售旅行社获得49份有效问卷。

结果显示, 排名前四的 CSF 是系统稳定性、系统可靠性、销售动态和产品线可用性。 通过关注这些 CSF, 旅游批发商可以最有效地分配其有限资源, 为其客户提供范围广泛的产品和服务, 改善电子采购服务, 并加强旅行社供应链中的组织间协作。

本研究开发了一个多方面的评估框架, 并确定了四个 CSF, 以协助在旅行社供应链中采用 B2B 电子采购系统。

The shipper selects a suitable shipping route and plans for a voyage in order to import and export cargo on the basis of published sailing schedules. The reliability of the sailing schedule will influence the shipper’s logistics expense, which means that the logistics costs will depend on the reliability of schedules published by container shipping companies. Therefore, it is important to consider factors which can cause delays would for container ships sailing on sea routes. The reliability of published sailing schedules can be affected by a number of different factors. This study adopts the multi-criteria decision making (MCDM) method to estimate the importance of the delaying factors in a sailing schedule. In addition, the consistent fuzzy preference relations (CFPR) method is applied to identify the subjective importance (weights) of the delaying factors. The entropy weight method combined with the actual performance of the container shipping company are both used when estimating the objective importance (weights) of the delaying factors. According to the analysis results, the criteria can be divided into four quadrants with different management implications, which indicate that instructions for chase strategy, sailing schedule control, fleet allocation, transship operation arrangement and planning for ports in routes are often ignored by container shipping companies. Container shipping companies should consider adjusting their operational strategies, which would greatly improve their operational performance.

This article has been withdrawn as it was published elsewhere and accidentally duplicated. The original article can be seen here: 10.1108/00022660710743840. When citing the article, please cite: J.L. Lin, C.Y. Wei, C.Y. Lin, (2007), “Aerodynamic performance of thin wings at low Reynolds numbers”, Aircraft Engineering and Aerospace Technology, Vol. 79 Iss 3 pp. 245 - 253.

Class imbalance learning, which exists in many domain problem datasets, is an important research topic in data mining and machine learning. One-class classification techniques, which aim to identify anomalies as the minority class from the normal data as the majority class, are one representative solution for class imbalanced datasets. Since one-class classifiers are trained using only normal data to create a decision boundary for later anomaly detection, the quality of the training set, i.e. the majority class, is one key factor that affects the performance of one-class classifiers.

In this paper, we focus on two data cleaning or preprocessing methods to address class imbalanced datasets. The first method examines whether performing instance selection to remove some noisy data from the majority class can improve the performance of one-class classifiers. The second method combines instance selection and missing value imputation, where the latter is used to handle incomplete datasets that contain missing values.

The experimental results are based on 44 class imbalanced datasets; three instance selection algorithms, including IB3, DROP3 and the GA, the CART decision tree for missing value imputation, and three one-class classifiers, which include OCSVM, IFOREST and LOF, show that if the instance selection algorithm is carefully chosen, performing this step could improve the quality of the training data, which makes one-class classifiers outperform the baselines without instance selection. Moreover, when class imbalanced datasets contain some missing values, combining missing value imputation and instance selection, regardless of which step is first performed, can maintain similar data quality as datasets without missing values.

The novelty of this paper is to investigate the effect of performing instance selection on the performance of one-class classifiers, which has never been done before. Moreover, this study is the first attempt to consider the scenario of missing values that exist in the training set for training one-class classifiers. In this case, performing missing value imputation and instance selection with different orders are compared.

Data mining is widely considered necessary in many business applications for effective decision-making. The importance of business data mining is reflected by the existence of numerous surveys in the literature focusing on the investigation of related works using data mining techniques for solving specific business problems. The purpose of this paper is to answer the following question: What are the widely used data mining techniques in business applications?

The aim of this paper is to examine related surveys in the literature and thus to identify the frequently applied data mining techniques. To ensure the recent relevance and quality of the conclusions, the criterion for selecting related studies are that the works be published in reputed journals within the past 10 years.

There are 33 different data mining techniques employed in eight different application areas. Most of them are supervised learning techniques and the application area where such techniques are most often seen is bankruptcy prediction, followed by the areas of customer relationship management, fraud detection, intrusion detection and recommender systems. Furthermore, the widely used ten data mining techniques for business applications are the decision tree (including C4.5 decision tree and classification and regression tree), genetic algorithm, k-nearest neighbor, multilayer perceptron neural network, naïve Bayes and support vector machine as the supervised learning techniques and association rule, expectation maximization and k-means as the unsupervised learning techniques.

The originality of this paper is to survey the recent 10 years of related survey and review articles about data mining in business applications to identify the most popular techniques.

Unique product design is a highlight of sustainable branding. The purpose of this paper is to investigate whether product design affects customers’ psychological responses (i.e. cognitive and affective responses) to smartphones, and, in turn, affects their brand loyalty (i.e. attitudinal and behavioral brand loyalty), further advancing the knowledge of product design and brand management.

This work used survey data from 456 Taiwanese with experience using smartphone. Structural equation modeling was employed to test the proposed model and hypotheses.

The results indicate that the product design significantly affects both cognitive response and affective response, which, in turn, significantly affect both attitudinal brand loyalty and behavioral brand loyalty. The findings also suggest that the moderating effect of product involvement on the relationship between product design and affective response is statistically significant, although it does not positively and significantly moderate the link between product design and cognitive response.

This study has two main limitations. First, this study was conducted in the context of smartphones, thus potentially constraining the generalization of the results to other industries. Second, the data in this study were obtained from a cross-sectional design.

These findings can permit companies to generate more brand loyalty in their customers and guide their management of assets and marketing activities.

This paper presents new insights into the nature and importance of product design in brand value.

This paper aims to investigate how the perception of physical coldness (vs warmth) influences consumers to make charitable donations.

Three experiments were conducted involving charitable donation scenarios.

Studies demonstrate that cold (vs warm) temperature cues result in greater intentions to donate to charities. Specifically, cold (vs warmth) cues activate the need for social connection which, in turn, motivate consumers to donate more money to charities. Furthermore, this effect holds even when the actual temperature instead of temperature cues is changed, and participants’ actual donation behavior instead of donation intentions is measured, thereby, strengthening the findings of this paper.

Boundary conditions associated with the effect of temperature cues need empirical investigation. Future research needs to investigate if the effect holds with variability of coldness. Future research also needs to determine whether the documented effect occur across various pro-social contexts.

The results suggest that non-profit organizations incorporate “cold” cues into advertisements (people feeling cold or cold landscapes) to increase monetary donations and that these organizations should focus on targeting donors during wintertime (vs summer time) to get more donations.

This is the first research to demonstrate the effects of temperature cues on charitable donations. The added value of this paper is the use of physical temperature change to highlight the phenomenon, and the link between cold (vs warm) temperature cue and the need of social connection.

The introduction of artificial intelligence (AI) technology has had a substantial influence on the retail industry. However, AI adoption entails considerable responsibilities and risks for senior managers. In this study, the authors developed an evaluation and selection mechanism for successful AI technology adoption in the retail industry. The multifaceted measurement and identification of critical factors (CFs) can enable retailers to adopt AI technology effectively and maintain a sustainable competitive advantage.

The evaluation and adoption of organisational AI technology involve multifaceted decision-making for management. Therefore, the authors used the analytic network process to develop an AI evaluation framework for calculating the weight and importance of each consideration. An expert questionnaire survey was distributed to senior retail managers and 17 valid responses were obtained. Finally, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to identify CFs for AI adoption.

The results revealed five CFs for AI adoption in the retail industry. The findings indicated that after AI adoption, top retail management is most concerned with factors pertaining to business performance and minor concerned about the internal system's functional efficiency. Retailers pay more attention to technology and organisation context, which are matters under the retailers' control, than to external uncontrollable environmental factors.

The authors developed an evaluation framework and identified CFs for AI technology adoption in the retail industry. In terms of practical application, the results of this study can help AI service providers understand the CFs of retailers when adopting AI. Moreover, retailers can use the proposed multifaceted evaluation framework to guide their adoption of AI technology.

The purpose of this paper is to demonstrate the applications of big data in personal knowledge management (PKM).

Five conventional knowledge management dimensions, namely, the value of data, data collection, data storage, data application and data presentation, were applied for integrating big data in the context of PKM.

This study concludes that time management, computer usage efficiency management, mobile device usage behavior management, health management and browser surfing management are areas where big data can be applied to PKM.

While the literature discusses PKM without considering the impact of big data, this paper aims to extend existing knowledge by demonstrating the application of big data in PKM.

This paper aims to provide thermal elastohydrodynamic lubrication (TEHL) contact model to study all balls’ lubrication performance of the ball screw when the multidirectional load is applied.

A new TEHL contact model combining the multidirectional load and the roughness surface texture is established to describe fatigue life of the ball screw. Meanwhile, the authors use the Reynolds equation to study the lubrication performance of the ball screw.

When the multidirectional load is applied, contact load, slide-roll ratio and entrainment velocity of all balls have a periodic shape. The TEHL performance values at the ball-screw contact points including contact stress, shear stress, minimum film thickness and temperature rise are higher than that at the ball-nut contact points. The TEHL performance values increase with the increase of root mean square (RMS) except for the film thickness. In addition, the radial load of the ball screw has a significant effect on the fatigue life.

The results of the studies demonstrate the new TEHL contact model that provides the instructive significance to analyze the fatigue life of the ball screw under the multidirectional load.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0097/