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The purpose of this paper is to analyze petite women’s body size and figure and investigate whether current petite sizing charts accurately reflect actual petit size women’s bodies. This study also categorizes petite women’s body shapes and suggests primary body measurements as a base size for each shape. The ultimate goal is to suggest fundamental body measurements for apparel companies to modify and improve their sizing.

This study used data from SizeUSA data to compare body measurements of 18-35-year-old petite women to regular women. The authors compared the results to measurement differences between petite and regular sizing charts of 14 apparel companies. Then, using the principal component analysis and cluster analysis, the authors classified petite women’s body shapes. Body measurements for each body type are contrasted with the current petite sizing charts, and then, the authors present differences as suggestions for modification and improvement of petite sizing.

Industry sizing system do not generally represent average petite size women preciously except for stature. Within the petite women, four body types were identified (top petite: 30.0 percent, bottom petite: 30.8 percent, regular petite: 23.6 percent, and plus size: 15.4 percent). Of the four groups, the ASTM D7878 generally represented the “top petite” sizing.

It is the first to analyze the industry petite sizing system utilizing population data and focus petite sizing for women aged 18-35. The authors believe this study could draw attention of the apparel industry, providing companies with ideas of how to improve their petite sizing for young women.

The purpose of this study is to reflect on Bowen and Chen’s study and provide insight for researchers to help them build loyalty models that will fit the consumer behavior of Millennials and managers as they build customer loyalty with Millennials. In 2001, Bowen and Chen developed and implemented a research framework for hotel managers to identify attributes that will increase customer loyalty. Since 2001, a major shift has taken place: demographically, as Baby Boomers retire and pass on, Millennials will become the dominant generational segment.

The authors use a review of literature to accomplish its purpose.

The authors argue that the loyalty models developed for Boomers will not be a good fit for Millennials. Three propositions are presented in the paper to help researchers develop models that will explain the customer loyalty of Millennials.

Implications are presented for researchers wishing to investigate the antecedents and consequences of loyalty for Millennials.

The transition from Boomers to Millennials creates a number of opportunities and challenges for managers, which are discussed in the paper.

There has been a dearth of empirical research on customer loyalty models developed for Millennials. This paper is a commentary on past models developed for Boomers and the transition needed to develop models for Millennials. It is hoped this dialogue will spawn research that develops loyalty models for Millennials.

This special “Anbar Abstracts” issue of the Marketing Intelligence & Planning is split into nine sections covering abstracts under the following headings: Marketing Strategy; Customer Service; Sales Management/Sundry; Promotion; Marketing Research/Customer Behaviour; Product Management; Logistics and Distribution.

The purpose of this paper is to investigate the relative importance of project managers' attitudes towards their project and their leadership competences for achieving project success. Leadership competences were assessed as emotional, managerial, and intellectual competences (EQ, MQ, IQ, respectively) using the leadership dimensions questionnaire. Attitudes were assessed through the importance project managers assign to the project success criteria.

Building on the competency school of leadership theories, this study used 400 responses to a global web‐based questionnaire to identify the variances in attitudes and leadership competences of project managers and its relation to project success. ANOVA and regression analyses were used to identify how attitudes and leadership competences related to project results.

The paper identifies two types of results variances, these are, variances in project results and variances in business results. The former is caused by the attitudes of project managers, the latter is caused by a mixture of their attitude and emotional competences.

The results show the relative importance of specific attitudes and leadership competences for different types of project success.

The results indicate key areas for project manager development in order to move from mediocre to superior project results.

The paper builds on prior work in EQ, MQ, and IQ for project management and is the first study to identify a migration theory for the combination of attitudes and leadership competences for project success.

This special “Anbar Abstracts” issue of the International Journal of Bank Marketing is split into five sections covering abstracts under the following headings: Marketing Strategy; Customer Service; Sales and Promotion; Product Development; Information Technology Strategies.

This paper aims to better jointly estimate Value at Risk (VaR) and expected shortfall (ES) by using the joint regression combined forecasting (JRCF) model.

Combining different forecasting models in financial risk measurement can improve their prediction accuracy by integrating the individual models’ information. This paper applies the JRCF model to measure VaR and ES at 5%, 2.5% and 1% probability levels in the Chinese stock market. While ES is not elicitable on its own, the joint elicitability property of VaR and ES is established by the joint consistent scoring functions, which further refines the ES’s backtest. In addition, a variety of backtesting and evaluation methods are used to analyze and compare the alternative risk measurement models.

The empirical results show that the JRCF model outperforms the competing models. Based on the evaluation results of the joint scoring functions, the proposed model obtains the minimum scoring function value compared to the individual forecasting models and the average combined forecasting model overall. Moreover, Murphy diagrams’ results further reveal that this model has consistent comparative advantages among all considered models.

The JRCF model of risk measures is proposed, and the application of the joint scoring functions of VaR and ES is expanded. Additionally, this paper comprehensively backtests and evaluates the competing risk models and examines the characteristics of Chinese financial market risks.

The purpose of this paper is to measure the systemic importance of industry in the world economic system under the system-wide event – the crisis of 2008-2009, by viewing this system as a weighted directed network of interconnected industries.

First, the authors investigate this crisis at three different levels based on network-related indicators: the “macro” global level, the “meso” country level, and the “micro” industry level. This investigation not only provides evidence for the systemic influence, that is, systemic risk, of the crisis, but also reveals the contagion mechanism of the crisis, which supports the stress testing. Second, the authors use a network-related business intelligence algorithm, the combined hyperlink-induced topic search (HITS) algorithm, to measure the contribution of a given individual industry to the overall risk of the economic system or, in other words, the systemic importance of the individual industry.

The HITS algorithm considers both the market information and the interconnectedness of the industries. Based on the stress testing, the performance of the combined HITS is compared with the purely market-based systemic risk measurement. The results show that the combined HITS outperforms the baseline in finding the top N systemically important industries.

The combined HITS algorithm provides a novel network-based perspective of systemic risk measurement.

Measuring the systemic importance based on the combined HITS algorithm can help managers and regulators design effective risk management policies. In this respect, the work initiates a research direction of studying the systemic risk in a business system based on a network-related business intelligence algorithm because the business system can be viewed as an interconnected network.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

This paper aims to solve the nonlinear problem in the uncertainty evaluation of the measurement of the positioning repeatability (RP) of industrial robots and provide guidance to restrict the uncertainty of measurement of RP (u_RP).

Firstly, some uncertain sources existing in the measurement procedure of RP are identified. Secondly, the probability distribution function (PDF) of every source is established on the basis of its measurements. Some spatial combined normal distributions are adopted. Then, a method, based on Monte Carlo method (MCM) and established measurement model, is developed for the estimation of u_RP. Thirdly, some tests are developed for the identification and validation of the selected PDFs of uncertain sources. Afterwards, the proposed method is applied for the evaluation and validation of the u_RP. Finally, influence analyses of some key factors are proposed for the quantification of their relative contributions to u_RP.

Results show that the proposed method can reasonably and objectively estimate the u_RP of the selected industrial robot, and changes of the industrial robots’ position and the laser trackers measurement are correlated. Additionally, the u_RP of the selected industrial robot can be restricted by using the results of its key factors on u_RP.

This paper proposes the spatial combined normal distribution to model the uncertainty of the repeatability of the laser tracker and industrial robot. Meanwhile, the proposed method and influence analyses can be used in estimating and restricting the u_RP and thus useful in determining whether the RP of a tested industrial robot meets its requirements.