Information Systems

IS/IT and strategy

A central theme in organizations concerns how they align their information systems strategy (including information and communications technology strategy) with the overall business strategy. Good alignment ensures, at least to some extent, that the information systems used support the organizational mission and goals. As a consequence, there is likely to be less conflict within organizations resulting from technology adoption and the success of the business strategy becomes feasible.

**Professors: **David Avison, Peter O'Connor, Jan Ondrus.

IS/IT evaluation, quality and security

Ever since the Chaos Report in the late 1990s, which suggested that many if not most information systems fail in practice at least to some extent, efforts have been made to improve the success rate of information systems in organizations. In one study of a telecommunications company carried out by researchers at ESSEC and elsewhere, an information systems failure is seen as causing the demise of the company. Evaluation research at ESSEC goes beyond narrow costs and benefits and into satisfying the needs of all stakeholders. In particular our research has shown the necessity of top management involvement. Through researching into the reasons for success and failure, the success rate of information systems has improved greatly over the last few years. Information systems need to be of excellent quality and have high security levels to be successful. We also develop quantitative approaches for assessing the quality of conceptual models, data, and more generally information systems.

**Professors:** David Avison, Isabelle Comyn-Wattiau, Nicolas Prat.

IS/IT engineering and development

The development of information systems is often seen as the "core" of the discipline of Information Systems. The study of methodologies, techniques and tools for developing and maintaining information systems is therefore a central theme of practice as well as research. We have developed a deep understanding of how information is represented inside information systems through different forms: models, programs, and data. This allows us to define reverse engineering and evolution methodologies for information systems.

**Professors:** David Avison, Dominique Briolat, Isabelle Comyn-Wattiau, Jan Ondrus, Nicolas Prat.

IS/IT adoption and diffusion

The study of IS innovation adoption is the investigation of how, why and at what rate the innovative ideas, technologies, and services are adopted through cultures and different demographic groups. The research is carried out at both the individual level (Technology Acceptance Model and Theory of Planned Behavior model) and the organizational level (diffusion of innovation theory).

**Professors:** David Avison, Dominique Briolat, Yan Li.

E-business and m-business

This domain of research focuses on the business issues raised by the growth in the use of the World Wide Web and the penetration of mobile phones in today's organizations. Faculty interests currently focus on issues in the travel e-distribution and e-marketing fields, and on the area of mobile payments.

**Professors:** Peter O’Connor, Jan Ondrus.

Knowledge management and decision support

This stream of research concentrates on systems that support managerial actions and decision-making. We conduct research on knowledge management, including the theory of knowledge management and specific technologies like mobile knowledge management. We have developed and are refining methodologies for designing data warehouses and multidimensional systems (OLAP-OnLine Analytical Processing). We have also applied multi-criteria analysis in the context of technology foresight.

**Professors:** Isabelle Comyn-Wattiau, Jan Ondrus, Nicolas Prat.

Research methods in IS

This research domain looks at the discipline of Information Systems as a whole. Although the impact of information systems on organizations, individuals and society is pervasive, Information Systems is the newest of the management disciplines and therefore there are lively debates on its practice, teaching and research. A central debate of the latter concerns the research methods used to study the domain. These include quantitative approaches, such as the analysis of survey material to find out in general terms what IS practitioners do, and in-depth qualitative research approaches such as case studies, action research and ethnography. The Department's researchers have impacted on a number of research themes in IS using a wide variety of appropriate research methods.

**Professors:** David Avison, Peter O’Connor.

# Decision Sciences

### Combinatorial optimization

Operations Research researchers in the Department mainly focus on modeling and solving hard combinatorial optimization problems with multiple binary decisions to be made (select or not?). The goal is to model these problems, analyze their complexity, and find exact methods (based on mathematical programming) or approximation methods (fast heuristics with worst-case performance guarantee). For solving the both empirical and theoretical validation of these methods is provided. The design and analysis of approximation methods is a core topic investigated by the research team.

**Professors:** Laurent Alfandari, Marc Demange.

Econometric theory

Econometrics is concerned with the tasks of developing and applying quantitative or statistical methods to the study and elucidation of economic principles. It combines economic or financial theory with probability, statistics and computer sciences to analyze and test relationships. Theoretical econometrics considers questions about the statistical properties of estimators and tests, while applied econometrics is concerned with the application of econometric methods to assess or evaluate theories.

Econometric applications are wide-ranging in social and management sciences, generally when the data are observational rather than obtained via a controlled experiment.

**Professors:** Guillaume Chevillon, Jean-Pierre Indjehagopian.

Forecasting

Forecasting is the process of estimation in unknown future situations. Its purpose is to generate information, such as probable values, and a range of possible alternatives, depending on the chosen criteria for evaluation of the accuracy, in a context where the future is, potentially loosely, related to present and past. Forecasting techniques rely on probability, statistics and econometrics, using multivariate time-series and survey data. The focus is on minimizing risk and uncertainty. Applications range from Economics to Finance, Marketing, business planning, and supply chain.

**Professors:** Guillaume Chevillon, Jean-Pierre Indjehagopian.

Mathematical statistics

Mathematical statistics deals with gaining information from data. In practice, data often contain some randomness or uncertainty. Mathematical statistics handles such data, describing them with a probabilistic model and using methods of probability theory as well as other branches of mathematics such as linear algebra and analysis. Mathematical statistics is the theoretical basis for many practices in applied statistics; it is impossible to understand and to use correctly these statistical techniques without a sound knowledge of probability theory and mathematical statistics.

**Professors:** Anne-Marie Dussaix, Jean-Pierre Indjehagopian, Marie Kratz.

Multivariate data analysis and collection

This research stream concentrates on collecting and analyzing huge masses of data where numerous variables are obtained for each properly chosen individual or statistical unit, e.g. answers of many respondents to a variety of questions on different themes in a survey, or different measurements of a company's performance in a study on the company's financial health. The challenge of disentangling complicated interrelationships among various measures and of interpreting both the analytical and the graphical results makes multivariate data analysis a rewarding activity for the investigator. Within the stream of multivariate data analysis, two main approaches are usually distinguished: exploratory and confirmatory. The exploratory, data-driven approach may help in discovering interesting and relevant correlations. It is appropriate when the objective is to identify the structure of the underlying factors that are responsible for co-variation in data with a focus on the variables (dimensionality reduction procedures), on the statistical units (clustering algorithms) or on both (e.g. tandem analysis). The confirmatory theory-driven approach, instead, adapts better to situations where the researcher has hypotheses to test on the number and nature of factors, the merging of available variables into blocks that are the expression of latent concepts, on their parameters and on a network of causal relationships connecting the different latent concepts in accordance with theoretical models. Tools for the modification, the improvement and the assessment of such models are provided in the light of interplay between theory and empirical data.

**Professors:** Anne-Marie Dussaix, Vincenzo Esposito Vinzi.

Operations Research models for management

Operations Research, the "science of better", deals with modeling optimal planning problems and finding efficient methods for solving them. It brings together Mathematics, Computer Science and Management. Management applications concern transportation (finding optimal routing of goods from delivery centers to clients), facility and plant location (finding the optimal location of one or several plants minimizing transportation costs between plants and clients), optimal sizing of recruitments and planning of human resources, network design, timetabling, production planning... Optimization models can be based on mathematical programming (linear/non linear programming) or graph theory.

**Professors:** Laurent Alfandari, Marc Demange.