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An approach is introduced to combine survey data with multi‐agent simulation models of consumer behaviour to study the diffusion process of organic food consumption. This methodology is based on rough set theory, which is able to translate survey data into behavioural rules. The topic of rule induction has been extensively investigated in other fields and in particular in learning machine, where several efficient algorithms have been proposed. However, the peculiarity of the rough set approach is that the inconsistencies in a data set about consumer behaviour are not aggregated or corrected since lower and upper approximation are computed. Thus, we expect that rough sets theory is suitable to extract knowledge in the form of rules within a consistent theoretical framework of consumer behaviour.

The purpose of this paper is to establish customers’ markets and rules of dynamic customer relationship management (CRM) systems for online retailers.

This research proposes a procedure to discover customers’ markets and rules, which adopts the recency, frequency, monetary value (RFM) variables, transaction records, and socioeconomic data of the online shoppers to be the research variables. The research methods aim at the supervised apriori algorithm, C5.0 decision tree algorithm, and RFM model.

This research discovered eight RFM markets and six rules of online retailers.

The proposed framework and research results can help retailer managers to retain and expand high value markets via their dynamic CRM and POS systems.

This research uses data mining technologies to extract high value markets and rules for marketing plans. The research variables are easy to obtain via retailers’ systems. The found customer values, RFM markets, shopping association rules, and marketing decision rules can be discovered via the framework of this research.

In the present time, there are large databases with parameters related to the maintenance of different equipment and installations. Given that manual analysis of sensors connected to machines is practically impossible, maintenance decisions from these databases can be difficult if the information automatically updated from these sensors is huge. Those great amounts of information are essentially useless if the knowledge contained inside cannot be extracted. Rough set theory facilitates this work by detecting those parameters that are truly significant for establishing the decision rules of the maintenance. In order to show the power of rough sets this paper contains a real case of a plastic injection installation for the analysis. Practical implications. An effective use of resource allocation in manufacturing processes could be achieved by using certain decision rules to indicate where and when maintenance decisions and tasks should be undertaken. This paper illustrates how the powerful theory of rough sets handles these issues. Therefore, the use of this technique is highly recommended for those industrial processes with a great amount of data and time (or in general, any resource) limitations.

A consulting team to an international food packaging company (SDYesBox) is attempting to decide which algorithm is the most useful for selecting two national markets in Central America and the Caribbean. SDYesBox wants to work closely with its immediate customers – manufacturers in the dairy and food industry and their customers (retailers) – to develop and market innovative products to low-income consumers in emerging markets; the “next big opportunity for the dairy industry” according to SDYesBox.

New product development and market selection in emerging markets in Latin America.

Five algorithms are “on the table” for assessing 14 countries by 12 performance indicators: weighted-benchmarking each country by the country leader's indicator scores; tallying by ignoring indicator weights and selecting the countries having the greatest number of positive standardized scores; applying a conjunctive and lexicographic combination algorithm; and using a “fluency metric” of how quickly consumers can say each country aloud. At least one member of the consulting team is championing one of these five algorithms. Which algorithm do you recommend? Why?

Learners gain skills, insights, and experience in alternative decision tools for evaluating and selecting choices among emerging markets to enter with new products for low-income (bottom of the pyramid) products ands services.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

The purpose of this paper is to discuss the current state of marketing analytics and how it should become a standard marketing research tool in the twenty‐first century.

The design of this paper is both a review of the field of marketing analytics and a discussion of how these factors must be enhanced and incorporated into twenty‐first century marketing research. As such this paper is offered as a viewpoint based on years of experience in the field and should serve as the basis for discussion and discourse by both academicians and practitioners.

In the realm of marketing, primary research has traditionally focused on quantitative or qualitative methodologies to provide customer insights. With advances in technology, especially data mining, marketing analytics has become an invaluable tool and should be viewed as an equal component of the marketing research toolkit. Analytics requires marketers to use data to understand customers at every touch point throughout their lifecycle with the business. To do this the analyst must mine, analyze, interpret, and present the information so that it is converted into actionable intelligence. In this process, the customer's information DNA is tracked, segmented, modeled and then acted upon. As these concepts and tools become standard operating procedures, academic marketing departments must internalize analytics into their overall curriculum in order to provide students with a compelling career advantage.

The value of this paper is that it presents marketers with a strong argument for the integration of marketing analytics into their practice of researching marketing issues and problems. Analytics completes the research triangle of qualitative, quantitative and data mined information gathering, analysis, and interpretation. It is hoped that this paper will generate additional discourse and research in this area and, especially, the adaptation of analytics as a standard research tool by marketers.

Students with emotional and behavioral disorders are at great risk for long-term negative outcomes. Researchers and practitioners alike acknowledge the need for evidence-based, preventive, and early intervention strategies. Accordingly, in this chapter an expanded view of prevention is presented as a series of data driven decisions to guide provision of supports that lessen the impact of emotional/behavioral disorders (EBD). Universal screening, use of a multitiered framework, delivery of increasingly intensive support prior to chronic and persistent patterns of behavior, and continuity of service across school, home, and community settings are discussed. Specific techniques for data decision-making, use of a school-based team approach, and recommendations for future research are also provided.

It should be re‐emphasized, however, that the [Vroom‐Yetton] model is explicitly normative in character in that it specifies what leaders should do in various organizational circumstances — rather than attempting to summarize what leaders do do and what the effects of those actions are. Thus, if the assumptions in the model about the outcomes which result from various leader behaviors are incorrect, the model will lead to faulty behavioral prescriptions.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.