Online Causal Structure Learning in the Presence of Latent Variables

We present two online causal structure learning algorithms which can track changes in a causal structure and process data in a dynamic real-time manner. Standard causal structure learning algorithms assume that causal structure does not change during the data collection process, but in real-world scenarios, it does often change. Therefore, it is inappropriate to handle such changes with existing batch-learning approaches, and instead, a structure should be learned in an online manner. The online causal structure learning algorithms we present here can revise correlation values without reprocessing the entire dataset and use an existing model to avoid relearning the causal links in the prior model, which still fit data. Proposed algorithms are tested on synthetic and real-world datasets, the latter being a seasonally adjusted commodity price index dataset for the U.S. The online causal structure learning algorithms outperformed standard FCI by a large margin in learning the changed causal structure correctly and efficiently when latent variables were present.Comment: 16 pages, 9 figures, 2 table

Strategic HRM Measurement in the 21st Century: From Justifying HR to Strategic Talent Leadership

Measurement will be vital to the evolution of human resource management in the coming century, but in this chapter we propose that it will not be measurement as usual. The future of HRM will require a decision science for talent resources that is as logical, reliable, consistent and flexible as Finance, the decision science for financial resources, and Marketing, the decision science for customer resources. In this chapter we describe the elements of this new decision science, which we call “Talentship,” and its implications for the future of strategic HR measurement. Using this framework, we review leading measurement approaches, describe their contributions, and identify the significant opportunities for improvement in future HR measurement systems

Open collaboration strategy of international retailers: an analysis of co-creator

Nowadays, online channels provide better distribution and communication strategies between companies and consumers. The importance of establishing online tools based on innovations and customer participation, is equally applicable to the international retail sector. Retail companies are able to reach consumers through their online channels, providing better ways to stand out from competitors. The options of joint open collaboration between international retails brands and its consumers implicate a transformation about the traditional communication between customers and companies. The objective of the present work is to analyze how the consumer experience is perceived after its participation in online co-creation actions, proposed by retail brands in the United Kingdom and Spain. The main purpose of this research is to consider how online co-creation initiatives, in the fashion industry sector, have a significant influence on co-creation experience, as well as, on relevant aspects, such as engagement or customer satisfaction.Ministerio de Economía y Competitividad. ECO 2014-55881-

An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection

The biological immune system (BIS) is characterized by networks of cells, tissues, and organs communicating and working in synchronization. It also has the ability to learn, recognize, and remember, thus providing the solid foundation for the development of Artificial Immune System (AIS). Since the emergence of AIS, it has proved itself as an area of computational intelligence. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. The V-Detectors algorithm depends greatly on the random detectors generated in monitoring the status of a system. These randomly generated detectors suffer from not been able to adequately cover the non-self space, which diminishes the detection performance of the V-Detectors algorithm. This research therefore proposed CSDE-V-Detectors which entail the use of the hybridization of Cuckoo Search (CS) and Differential Evolution (DE) in optimizing the random detectors of the V-Detectors. The DE is integrated with CS at the population initialization by distributing the population linearly. This linear distribution gives the population a unique, stable, and progressive distribution process. Thus, each individual detector is characteristically different from the other detectors. CSDE capabilities of global search, and use of L´evy flight facilitates the effectiveness of the detector set in the search space. In comparison with V-Detectors, cuckoo search, differential evolution, support vector machine, artificial neural network, na¨ıve bayes, and k-NN, experimental results demonstrates that CSDE-V-Detectors outperforms other algorithms with an average detection rate of 95:30% on all the datasets. This signifies that CSDE-V-Detectors can efficiently attain highest detection rates and lowest false alarm rates for anomaly detection. Thus, the optimization of the randomly detectors of V-Detectors algorithm with CSDE is proficient and suitable for anomaly detection tasks

Retail positioning through customer satisfaction: an alternative explanation to the resource-based view

Through exploring factors influencing effective retail positioning strategies in an emerging market environment, this paper challenges the role of isolation mechanism and heterogeneous idiosyncrasy argued by the resource-based view theory. By drawing on a sample of 11,577 customers from hypermarkets, electronic appliance specialty stores and department stores in major Chinese cities, we set up ten hypotheses and confirm a nine-item model for customeroriented retail positioning (perceived price, store image, product, shopping environment, customer service, payment process, after-sales service, store policies, and shopping convenience). Our results show that different retail formats achieve success through the implementation of similar positioning strategies, in which case, it is not heterogeneity but homogeneity that contributes to retailers' success greatly at the development stage of retail expansion. Our results challenge previously proved effectiveness of inimitability to success by the resource-based view, and support homogenous idiosyncrasy of retailers in the implementation of customer-oriented positioning strategies in an emerging market

Marketing competition on a new product introduction - a structural analysis using systems thinking

Launching a new product on the market is a strategic activity that needs specific investments and a specific organisation. There are multiple factors that determine the success of a new product on the market but their direct effects are not often very well observable (marketing for example). With this study, we analysed the systemic structure underlying the dynamics related to the introduction of a new product on the market. In particular, we built a qualitative model based on the systems thinking methodology of causal-loop diagrams (CLDs), starting from the main structure and assumptions of the well-known Bass model. The model provides a systemic perspective on the interdependencies among various aspects that interact in important organisational areas. The presented causal-loop diagram tries to describe the systems structure which is intrinsic to the introduction and diffusion of a new product on the market, and how ultimately the related dynamics could be manage

Analytics and complexity: learning and leading for the future

There is growing interest in the application of learning analytics to manage, inform and improve learning and teaching within higher education. In particular, learning analytics is seen as enabling data-driven decision making as universities are seeking to respond a range of significant challenges that are reshaping the higher education landscape. Experience over four years with a project exploring the use of learning analytics to improve learning and teaching at a particular university has, however, revealed a much more complex reality that potentially limits the value of some analytics-based strategies. This paper uses this experience with over 80,000 students across three learning management systems, combined with literature from complex adaptive systems and learning analytics to identify the source and nature of these limitations along with a suggested path forward

save to DISC: Documenting Innovation in Music Learning

The paper discusses an approach to determining the worth and value of innovation in music education and measuring it’s capacity for meaning and engagement. It also aims to identify new examples of innovation across a broad range of music learning contexts and establish a rigorous digital process for documenting, evaluating and distributing innovative cases and resources for present and future contexts. It discusses specifically a pilot project that seeks to document innovation in sound curriculum (DISC). save to DISC is an exploratory study in an Australasian CRC for Interaction Design (ACID) project that proposes to establish flexible and effective procedures for the sourcing, evaluating, refereeing, editing, producing, validating, storing, publishing, and distributing of a wide range of media and content types. It involves documenting innovative and successful practice in music education, creating and evaluating programs in difficult/challenging school contexts and commissioning and encouraging the production of resource materials for 21 st century contexts

COBRA framework to evaluate e-government services: A citizen-centric perspective

E-government services involve many stakeholders who have different objectives that can have an impact on success. Among these stakeholders, citizens are the primary stakeholders of government activities. Accordingly, their satisfaction plays an important role in e-government success. Although several models have been proposed to assess the success of e-government services through measuring users' satisfaction levels, they fail to provide a comprehensive evaluation model. This study provides an insight and critical analysis of the extant literature to identify the most critical factors and their manifested variables for user satisfaction in the provision of e-government services. The various manifested variables are then grouped into a new quantitative analysis framework consisting of four main constructs: cost; benefit; risk and opportunity (COBRA) by analogy to the well-known SWOT qualitative analysis framework. The COBRA measurement scale is developed, tested, refined and validated on a sample group of e-government service users in Turkey. A structured equation model is used to establish relationships among the identified constructs, associated variables and users' satisfaction. The results confirm that COBRA framework is a useful approach for evaluating the success of e-government services from citizens' perspective and it can be generalised to other perspectives and measurement contexts. Crown Copyright © 2014.PIAP-GA-2008-230658) from the European Union Framework Program and another grant (NPRP 09-1023-5-158) from the Qatar National Research Fund (amember of Qatar Foundation