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The purpose of this paper is to examine the relationship between customer order lead time (COLT) and the price sensitivity of an electrical equipment manufacturer company. In consequence, it examines two research questions in terms of COLT, price and profitability level and to ensure the validity and practical justification of these research questions.

In this research the authors have used a case study approach where three business measures, namely COLT, price and the profitability level of a case company were investigated and analyzed critically. These measures were implemented through four different customer segments with two production lines of the case company. Data were collected from the company's order delivery database from the period 2006 to 2008. In addition, different experimental data were collected through interviewing and reviewing the results of the data analysis with the unit managers.

In this paper the authors have observed the correlation between the price, profit and COLT with all four customer segments in both the production lines of the case company. From the case data, the authors concluded that the customer did not pay more when the COLT is shorter than with the average time. It is also noticeable that the profit margin is higher for the case company to handle COLT with shorter lead time than the average order delivery lead time.

More case examples might be helpful to motivate the managers to accept the research outcomes.

The concept of the company's COLT in relation to the price and profitability level supports organizational managers in their decision‐making process in terms of productivity level and the company's growth. It will motivate the managers to make tradeoffs among various developmental measures.

This paper implemented a unique approach for measuring the significant level of price and profitability level over COLT. From the outcomes of this study, it is observed that the price correlated positively with the COLT and has a direct and significant impact on it. When the price is increased the COLT is also increased. It is also noticed that the products of the case company which offered shorter lead times were on average also more profitable, even though there were no significant differences in average pricing between the customer segments.

Steel Corp has a large production capacity but a shrinking steel market in Europe. Reaching growing markets like China and U.A.E will be important to sustaining and growing revenue but is tough due to higher transportation costs. In this case, users must identify and use logistics data; logistics customer segmentation and related cost analysis.

To determine the status of internet and e‐commerce adoption by the Taiwan semiconductor industry, the research is designed to help government and enterprise in formulating strategic plans and making resource allocation decisions.

Using the three‐level model of internet commerce adoption (MICA), a survey of 287 companies and web sites was designed. Semiconductor firms were placed into five categories: integrated circuit (IC) design, manufacturing, packaging, IC testing, and peripheral device manufacturing.

The MICA model shows the internet adoption ratio for semiconductor firms as 82.6 percent, significantly higher than the electronics and electrical machinery industry sector (56.5 percent). The IC manufacturing and packaging segment are in the processing stage, the final stage of development for the MICA model. One‐third of the IC testing industry segment falls into the provision stage, and 36.1 percent web sites are in the processing stage. The IC design and peripherals industrial segments are located in the provision stage.

The IC manufacturing segment is conducting more financial transactions than the other segments – a result that matches earlier research showing that larger companies are most likely to implement e‐business applications. Many enterprises in the industry are lagging with the adoption of the internet indicating a need for education and training.

This benchmark study provides a framework for evaluating the internet adoption status of semiconductor and other high technology firms. The MICA model is demonstrated to be suitable for evaluating the different stages of internet adoption.

Dynamic market segmentation is a very important topic in many businesses where it is interesting to gain knowledge on the reference market and on its evolution over time. Various papers in the reference literature are devoted to the topic and different statistical models are proposed. The purpose of this paper is to compare two statistical approaches to model categorical longitudinal data to perform dynamic market segmentation.

The latent class Markov model identifies a latent variable whose states represent market segments at an initial point in time, customers can switch to one segment to another between consecutive measurement occasions and a regression structure models the effects of covariates, describing customers’ characteristics, on segments belonging and transition probabilities. The latent class growth approach models individual trajectories, describing a behaviour over time. Customers’ characteristics may be inserted in the model to affect trajectories that may vary across latent groups, in the author’s case, market segments.

The two approaches revealed both suitable for dynamic market segmentation. The advice to marketer analysts is to explore both solutions to dynamically segment the reference market. The best approach will be then judged in terms of fit, substantial results and assumptions on the reference market.

The proposed statistical models are new in the field of financial markets.

Elder German grocery shoppers are a growing, heterogeneous, and highly relevant and attractive, but under-researched market segment. In order to understand them and their grocery shopping motivations better and target them efficiently and effectively, the purpose of this paper is to identify dimensions of their shopping motivations and segment them based on these dimensions.

In total, 26 grocery store-choice criteria were identified in a thorough literature review and focus-group interviews with 36 elder German consumers aged 65 and older. In a subsequent survey, the importance of these criteria was rated by 1,288 German shoppers of the same age group. A principal component and cluster analysis were performed to identify dimensions of store-choice criteria and segments of elder German grocery shoppers. Multivariate analysis of variance, analysis of variance and discriminant analysis were used to test for statistically significant differences between the clusters.

Basic quality considerations, shopping experience and social interaction, service and assistance, price consciousness, product orientation, convenient location and quick service and packaging requirements influence the grocery store choice of elder German consumers in decreasing order of variance explained. The cluster analysis revealed indifferent, leisure, convenience, assistance-oriented, no frills, product-oriented and service-oriented elder German shoppers, which differ in their shopping motivations statistically and significantly. These clusters are described and contrasted in detail to derive managerial implications.

This research provides the first store-choice component analysis and cluster analysis for elder German grocery shoppers. This can help food retail to reach this attractive target group more efficiently and effectively and improve the food supply of elder German consumers.

Since its conception over 60 years ago by Frederick in 1934, the concept of segmentation has gained increasing importance, in both the consumer and the business domains. Examination of research within the latter domain indicates that, although considerable amounts of research have been carried out, these efforts appear to focus on sub‐areas of segmentation such as the development of segmentation bases and models, at the expense of a more strategic view. This not only has resulted in a diffused understanding of the subject‐matter but also is posited to have slowed the progress of theory development and research in business segmentation. Presents a comprehensive conceptualisation of business segmentation in the form of an integrating framework of business segmentation, aimed at raising new research agendas which could lead to a better understanding of existing gaps between theory and implementation and better recommendations to practitioners and assisting further development of theory in business segmentation.

While marketers want to drive higher repurchases for better business sustainability, repeat shopping experiences may change customer perceptions of the online channel, resulting in the emergence of new segment typologies. Therefore, the purpose of this paper is to explore the segmentation of online clothing shoppers using a repeat online clothing shopper base. Further, it analyses segment positions in a perceptual space to derive relevant positioning insights for the various segments.

Segmentation is done using dual bases of e-lifestyle and website quality factors for which the scales are derived from literature and then adapted and validated using a two-phase process across two samples of 271 and 644 experienced shoppers, respectively, in India. Positions of the segments are explored using the discriminant analysis-based perceptual mapping technique.

Three segments are found, namely disengaged averse online shoppers, interactive convenience seekers and adept online shopping optimists with the latter two having a higher propensity to purchase clothes online. Perceptual mapping of the segment positions reveals dimensions, which can be used for appropriate positioning.

The research methodology may be replicated for other products and country contexts, and additional factors may be explored for further insights.

The study reveals insights on the evolving nature of segments as shoppers gain experience of online shopping for clothes and highlights the varied reasons for the growing acceptability of the online channel. The findings reveal key targeting and positioning strategies for e-marketers.

This is one of the first studies of its kind in India, which explores the segmentation of repeat online clothing shoppers in India using dual bases. Another distinctive feature of the study is its use of the perceptual mapping technique to draw inferences about factors that differentiate multi-segment buying behavior.

This study attempts to segment the Swiss travel market based on holiday activities. It is based on data of the 2001 travel market in Switzerland. Cluster and discriminant analysis have been employed in order to segment the data and to explain the differences between the clusters. Hereby, five activity‐clusters could be defined, each representing a set of holiday activities most likely to be exercised. The analysis of the five clusters revealed that two demographic profile variables “occupation” and “size of household” did explain the affiliation to a certain cluster. The same could be found for the following travel profile variables: “destination and duration of the trip”, “total number of participants from a household and “type of trip”. Further research will be necessary to find out if the clusters identified really do fulfil the needed criteria for market segments in order to be used by companies in the travel industry.

The purpose of this paper is to calculate the Hugoniot relations of polyurea; also to investigate the atomic-scale energy change, the related chain conformation evolution and the hydrogen bond dissociation of polyurea under high-speed shock.

The atomic-scale simulations are achieved by molecular dynamics (MD). Both non-equilibrium MD and multi-scale shock technique are used to simulate the high-speed shock. The energy dissipation is theoretically derived by the thermodynamic and the Hugoniot relations. The distributions of bond length, angle and dihedral angle are used to characterize the chain conformation evolution. The hydrogen bonds are determined by a geometrical criterion.

The Hugoniot relations calculated are in good agreement with the experimental data. It is found that under the same impact pressure, polyurea with lower hard segment content has higher energy dissipation during the shock-release process. The primary energy dissipation way is the heat dissipation caused by the increase of kinetic energy. Unlike tensile simulation, the molecular potential increment is mainly divided into the increments of the bond energy, angle energy and dihedral angle energy under shock loading and is mostly stored in the soft segments. The hydrogen bond potential increment only accounts for about 1% of the internal energy increment under high-speed shock.

The simulation results are meaningful for understanding and evaluating the energy dissipation mechanism of polyurea under shock loading, and could provide a reference for material design.

To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation.

Researchers asked about innovation goals, barriers to innovation, metrics used to measure innovation outcomes, treatment and types of innovation projects and the role of big data and analytics in innovation processes.

Three distinct groups emerged: Leaders, Strivers and Strugglers. Leaders are markedly different as a group: they innovate using big data and analytics within a structured approach, and they focus in particular on collaboration.

Respondents were from the 2014 IBM Innovation Survey. We conducted cluster analysis with 81 variables. The three cluster solution was determined deploying latent class analysis (LCA), a family of techniques based around clustering and data reduction for segmentation projects. It uses a number of underlying statistical models to capture differences between observed data or stimuli in the form of discrete (unordered) population segments; group segments; ordered factors (segments with an underlying numeric order); continuous factors; or mixtures of the above.

Leaders don’t just embrace analytics and actionable insights; they take them to the next level, integrating analytics and insights with innovation. Leaders follow three basic strategies that center on data, skills and tools and culture: promote excellent data quality and accessibility; make analytics and innovation a part of every role; build a quantitative innovation culture.

The research found that leaders leverage big data and analytics more effectively over a wider range of organizational processes and functions. They are significantly better at leveraging big data and analytics throughout the innovation process – from conceiving new ideas to creating new business models and developing new products and services.