Ecole de Commerce IDRAC
Recent publications
Technology forecasting is a preliminary step in understanding social change. The response to COVID-19 will affect retailers and customers for years to come, forcing changes to interactions between individuals and technology. Innovative technologies that interrelate social and technological factors merit a re-examination, to explain the impact on consumer behavior where ‘physical’ and ‘digital’ are brought together. This paper explores the use of haptic rendering stimulation for pre-purchase decision-making. The objective is to identify how touching an interface can influence product evaluation and purchase intention. Drawing from an exploratory experimental design, the findings show the importance of interface touch for inferring product information or pleasure to interact with the product, confirming the relationship between knowledge and mental representation, body sensory-motor actions and online shopping contexts.
Purpose In today’s global economy, high in talent but low in growth, the capability and skills mismatch between the output of universities and the demands of business has escalated to a worrying extent for graduates. Increasingly, university students are considering alternatives to a lifetime of employment, including their own start-up, and becoming an entrepreneur. The literature indicates a significant disconnect between the role and value of education and healthy enterprising economies, with many less-educated economies growing faster than more knowledgeable ones. Moreover, theory concerning the entrepreneurial pipeline and entrepreneurial ecosystems is applied to graduate entrepreneurial intentions and aspirations. Design/methodology/approach Using on a large-scale online quantitative survey, this study explores graduate ‘entrepreneurial intention’ in the UK and France, taking into consideration personal, social and situational factors. The results point to a number of factors that contribute to entrepreneurial intention including social background, parental occupation, gender, subject of study, and nationality. The study furthers the understanding of and contributes to the extant literature on graduate entrepreneurship. It provides an original insight into a topical and contemporary issue, raising a number of research questions for future study. Findings For too long, students have been educated to be employees, not entrepreneurs. The study points strongly to the fact that today’s students have both willingness and intention to become entrepreneurs. However, the range of pedagogical and curriculum content does not correspond with the ambition of those who wish to develop entrepreneurial skills. There is an urgent need for directors of higher education and pedagogues to rethink their education offer in order to create a generation of entrepreneurs for tomorrow’s business world. The challenge will be to integrate two key considerations: how to create a business idea and how to make it happen practically and theoretically. Clearly, change in the education product will necessitate change in the HE business model. Research limitations/implications The data set collected was extensive (c3500), with a focus on France and the UK. More business, engineering and technology students completed the survey than others. Further research is being undertaken to look at other countries (and continents) to test the value of extrapolation of findings. Initial results parallel those described in this paper. Practical implications Some things can be taught, others need nurturing. Entrepreneurship involves a complex set of processes which engender individual development, and are highly personalised. Higher Education Enterprise and Teaching and Learning Strategies need to be cognisant of this, and to develop innovative and appropriate curricula, including assessment, which reflects the importance of the process as much as that of the destination. Originality/value This work builds on an extensive literature review coupled with original primary research. The authors originate from a variety of backgrounds and disciplines, and the result is a very challenging set of thoughts, comments and suggestions that are relevant to all higher education institutions, at policy, strategy and operational levels.
In this article we construct bivariate discrete distributions in . We make use of a generalized trivariate reduction technique. The special case leading to a generalization of a bivariate Skellam distribution is studied in detail. Properties of the derived models as well as estimation are examined. Real data application is provided. Discussion of extensions to different models is also mentioned.
In this paper, we introduce a new distribution on , which can be viewed as a natural bivariate extension of the Skellam distribution. The main feature of this distribution a possible dependence of the univariate components, both following univariate Skellam distributions. We explore various properties of the distribution and investigate the estimation of the unknown parameters via the method of moments and maximum likelihood. In the experimental section, we illustrate our theory. First, we compare the performance of the estimators by means of a simulation study. In the second part, we present two applications to a real data set and show how an improved fit can be achieved by estimating mixture distributions.
A convolution regression model with random design is considered. We investigate the estimation of the derivatives of an unknown function, element of the convolution product. We introduce new estimators based on wavelet methods and provide theoretical guarantees on their good performances.
We investigate the estimation of the density-weighted average derivative from biased data. An estimator integrating a plug-in approach and wavelet projections is constructed. We prove that it attains the parametric rate of convergence 1/n under the mean squared error.
The problem of estimating the density-weighted average derivative of a regression function is considered. We present a new consistent estimator based on a plug-in ap-proach and wavelet projections. Its performances are explored under various depen-dence structures on the observations: the independent case, the ρ-mixing case and the α-mixing case. More precisely, denoting n the number of observations, in the indepen-dent case, we prove that it attains 1/n under the mean squared error, in the ρ-mixing case, 1/ √ n under the mean absolute error, and in the α-mixing case, and ln n/n under the mean absolute error.
In recent years, there has been a growing interest in modelling integred-valued time series. In this article, we propose a modified and generalized version of the first order rounded integer-valued autoregressive RINAR(1) model, originally introduced by Kachour and Yao (200912. Kachour , M. , Yao , J. F. ( 2009 ). First-order rounded integer-valued autoregressive (RINAR(1)) process . Journal of Time Series Analysis 30 ( 4 ): 417 – 448 . [CrossRef], [Web of Science ®]View all references). Indeed, this class can be considered as an alternative of classical models based on the thinning operators. Using a Markov chain method, conditions for stationarity and the existence of moments are investigated. Least squares estimator of the model parameters is considered and its consistence is established. Finally, we describe the price change data using a model of the new class.
A single-machine multi-product lot-sizing and sequencing problem is studied. In this problem, items of n different products are manufactured in lots. Demands for products as well as per item processing times are known. There are losses of productivity because of non perfect production. There is also a sequence dependent set-up time between lots of different products. Machine yields and product lead times are assumed to be known deterministic functions. The objective is to minimize the cost of the demand dissatisfaction provided that the total processing time does not exceed a given time limit. We propose two integer linear programming (ILP) models for the NP-hard “fraction defective” case of this problem and compare effectiveness of their ILOG CPLEX realizations with a dynamic programming algorithm in a computer experiment. We also show how an earlier developed fully polynomial time approximation scheme (FPTAS) and one of the ILP models can be extended for a more complex case.
In recent years, many attempts have been made to find accurate models for integer-valued times series. The SINAR (for Signed INteger-valued AutoRegressive) process is one of the most interesting. Indeed, the SINAR model allows negative values both for the series and its autocorrelation function. In this paper, we focus on the simplest SINAR(1) model under some parametric assumptions. Explicitly, we give an implicit form of the stationary distribution for a known innovation. Simulation experiments as well as analysis of real data sets are carried out to attest the models performance.
We compare three frequently used volatility modelling techniques: GARCH, Markovian switching and cumulative daily volatility models. Our primary goal is to highlight a practical and systematic way to measure the relative effectiveness of these techniques. Evaluation comprises the analysis of the validity of the statistical requirements of the various models and their performance in simple options hedging strategies. The latter puts them to test in a “real life” application. Though there was not much difference between the three techniques, a tendency in favour of the cumulative daily volatility estimates, based on tick data, seems clear. As the improvement is not very big, the message for the practitioner — out of the restricted evidence of our experiment — is that he will probably not be losing much if working with the Markovian switching method. This highlights that, in terms of volatility estimation, no clear winner exists among the more sophisticated techniques. Key wordsCumulative daily volatility–GARCH–Markovian switching–standardised returns–volatility hedging–volatility modelling
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399 members
Jean-Michel Sahut
  • School of Business
Mohamed Firas Thraya
  • Department of Finance
Hugo Lamy
  • School of Business
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