Institut Supérieur de Gestion de Tunis

Requirements capture is arguably the most important step in software engineering, and yet the most difficult and the least formalized one [Phalp and Shepperd 2000]. Enterprises build information systems to support their business processes . Software engineering research has typically focused on the development process, starting with user requirements—if that—with business modeling often confused with software system modeling [Isoda 2001]. Researchers and practitioners in management information systems have long recognized that understanding the business processes that an information system must support is key to eliciting the needs of its users (see e.g., Eriksson and Penker 2000]), but lacked the tools to model such business processes or to relate such models to software requirements. Researchers and practitioners in business administration have long been interested in modeling the processes of organizations for the purposes of understanding, analyzing, and improving such processes [Hammer and Champy 1993], but their models were often too coarse to be of use to software engineers. The advent of ecommerce and workflow management systems, among other things, has led to a convergence of interests and tools, within the broad IT community, for modeling and enabling business processes. In this article we present an overview of business process modeling languages. We first propose a categorization of the various languages and then describe representative languages from each family.

Purpose – The purpose of this paper is to investigate the impact of religiosity on consumer attitudes and purchase intentions toward Islamic banks. Design/methodology/approach – The study takes place in the Tunisian context. Even though Tunisia is a Muslim country, the culture is considerably different from those of the Middle East or Malaysia (countries where the majority of studies on Islamic banks have taken place). Consequently, an adapted religiosity scale was developed to fit the study’s context. Then, the scale was pre-tested on a sample of 188 respondents. In order to test the research hypotheses, a second data collection, based on a convenience sampling technique, was undertaken, yielding a sample of 217 respondents. Findings – The religiosity variable was found to be tridimensional. Results show that the more a person fears divine punishment, the more he/she will develop a favorable attitude towards Islamic banks. Also, the more a person believes in Islamic laws, the more favorable his/her attitude towards Islamic banks. However, the relationship between religious involvement (practice and interest) and attitude toward Islamic banks is found to be insignificant. Other alternative models were tested and the results indicate that neither fear, nor beliefs, nor religious involvement has a direct effect on purchase intention. Thus, religiosity has an indirect effect on purchase intentions of Islamic bank services through attitude towards these banks. Practical implications – It is suggested that a communication strategy focusing on the compatibility of Islamic banks with Islamic beliefs and eliminating any doubt that Islamic bank operations are suspicious (from a religious point of view) could attract a segment of consumers who wish to be in harmony with the prescriptions of their religion. Islamic banks can better position their offers compared to conventional banks and improve the perception of actual or potential clients. The study offers some implications to managers of conventional banks as well. Originality/value – Previous studies have reported the strong impact of religion on Muslims’ attitude towards Islamic banks. The main contribution of this study is to show which dimension of religiosity has the most important impact on attitude and purchase intention toward Islamic banks.

Information systems are frequently exposed to various types of threats which can cause different types of damages that might lead to significant financial losses. Information security damages can range from small losses to entire information system destruction. The effects of various threats vary considerably: some affect the confidentiality or integrity of data while others affect the availability of a system. Currently, organizations are struggling to understand what the threats to their information assets are and how to obtain the necessary means to combat them which continues to pose a challenge. To improve our understanding of security threats, we propose a security threat classification model which allows us to study the threats class impact instead of a threat impact as a threat varies over time. This paper addresses different criteria of information system security risks classification and gives a review of most threats classification models. We define a hybrid model for information system security threat classification in order to propose a classification architecture that supports all threat classification principles and helps organizations implement their information security strategies.

This paper presents a method for assessing the reliability of a sensor in a classification problem based on the transferable belief model. First, we develop a method for the evaluation of the reliability of a sensor when considered alone. The method is based on finding the discounting factor minimizing the distance between the pignistic probabilities computed from the discounted beliefs and the actual values of data. Next, we develop a method for assessing the reliability of several sensors that are supposed to work jointly and their readings are aggregated. The discounting factors are computed on the basis of minimizing the distance between the pignistic probabilities computed from the combined discounted belief functions and the actual values of data.

The proliferation of accounting scandals has prompted the need to improve the relevance of financial reporting by setting up good governance structures. The relationship between governance and information quality has been strongly debated in the context of developed countries. It is only recently that attention turned to the study of governance and financial disclosure in emerging countries.In this paper, we examine the effect of the governance mechanisms on the financial reporting quality for a sample of Tunisian firms. Specifically, we focus on the characteristics of the board of directors and the ownership structure of the firms listed on the Tunis Stock Exchange during the period 1997–2007. The results reveal that the governance mechanisms affect the financial information quality of the Tunisian companies. Particularly, the power of the foreigners, the families and the blockholders reduces the reporting quality, while the control by the State and the financial institutions is associated with a good quality of financial disclosure.

Traditionally, the processing times of jobs are assumed to be fixed and known throughout the entire process. However, recent empirical research in several industries has demonstrated that processing times decline as workers improve their skills and gain experience after doing the same task for a long time. This phenomenon is known as learning effects. Recently, several researchers have devoted a lot of effort on scheduling problems under learning effects. Although there is increase in the number of research in this topic, there are few review papers. The most recent one considers solely studies on scheduling problems with learning effects models prior to early 2007. For that, this paper focuses on reviewing the most recent advances in this field. First, we attempt to present a concise overview of some important learning models. Second, a new classification scheme for the different model of scheduling under learning effects is proposed and discussed. Next, a cartography showing the relation between some well-known models is proposed. Finally, our viewpoints and several areas for future research are provided.

This article aims to provide a bibliometric analysis of intellectual capital research published in the Scopus database from 1956 to 2020 to trace the development of scientific activities that can pave the way for future studies by shedding light on the gaps in the field. The analysis focuses on 638 intellectual capital-related papers published in the Scopus database over 60 years, drawing upon a bibliometric analysis using VOSviewer. This paper highlights the mainstream of the current research in the intellectual capital field, based on the Scopus database, by presenting a detailed bibliometric analysis of the trend and development of intellectual capital research in the past six decades, including journals, authors, countries, institutes, co-occurrence, and author’s keywords. The findings imply that intellectual capital researchers do not use broad relevant theories and findings from studies beyond their clusters. Another result is that developing nations continue to be underexplored in terms of intellectual property research due to a lack of trust representation and a lack of appropriate investigators. Finally, the data analysis identifies a number of potential research issues to be investigated regarding intellectual capital development, which serve as raw material for future research. Once again, this study provides a framework for firms to build and implement intellectual capital development plans.

Purpose The purpose of this paper is twofold: first, the authors empirically examine the effect of the perceived values of mobile banking (MB) applications on customers’ e-trust, e-satisfaction and e-loyalty and second, the authors investigate the moderating role of customers’ age in the relationship between perceived value of MB and e-trust. Design/methodology/approach A survey was conducted on a sample of 361 Tunisian banks’ customers using mobile applications. Structural equation modeling method was performed to test the research hypotheses. Findings The results of this research revealed that quality, price and emotional perceived values’ dimensions of MB applications are predictors of customers’ e-trust. Within this spectrum, age acts as a moderator in these latter relationships. Findings also suggested that e-trust has positive effects on e-satisfaction and e-loyalty. Practical implications This paper presented a reading grid for bank marketers allowing them to identify the levers they must focus their efforts on so as to retain the customers using MB applications. It also permits them to identify the needs and values of three generation of customers, namely baby boomers, generation X and generation Y, so that they can offer better services for each group. Originality/value This paper sheds light on an innovative and unexplored topic in the literature. The authors broaden understanding of the relationship between the perceived values of MB applications, e-loyalty, e-trust and e-satisfaction. Research devoted to investigate these different links through an integrative model does exist, yet embryonic. This study also highlights differences between three group of customer, namely baby boomers, generation X and generation Y in terms of perceived values that builds their trust in the context of MB applications use.

Purpose The purpose of this study is to examine the effect of the transformational and transactional leadership styles and its sub-dimensions on exploitative and exploratory innovations. The authors deepened the analysis by highlighting the contribution of ambidextrous leadership on ambidextrous innovation. Design/methodology/approach The conceptual model defined through a thorough review of the literature is tested empirically on a sample of 265 senior managers working in Tunisian knowledge-intensive firms (KIFs). A quantitative analysis is performed via a questionnaire. The analysis of the data collected is carried out using the partial least squares method. Findings Results show that exploratory innovation is linked to transformational leadership and in particular to individualized consideration, intellectual stimulation and inspirational motivation. Moreover, exploitative innovation is promoted by transformational and transactional leadership styles and particularly by attributed idealized influence and contingent rewards. Also, ambidextrous leadership affects positively ambidextrous innovation. Originality/value While little studies have investigated how the transformational and transactional leadership styles as constructs of second-order influence differently exploitative innovation and exploratory innovation, this research deepens the analysis by focusing on sub-dimensions of leadership styles that promote each type of innovation within KIFs. It also pioneers the investigation of the effect of ambidextrous leadership on ambidextrous innovation that has never been explored. This research provides important insights for leaders of KIFs and sheds the light on the key aspects of leadership that boost ambidextrous innovation.

Reports a common occurrence in the marketplace: brand imitation. A total of 160 consumers participated in an experiment where goodness of imitation, presence/absence of the imitated brand, reputation of the store and type of product (convenience/luxury) were manipulated. The results show that consumer evaluations of brand imitations do not depend on how good the imitation is. The better the image of the store in which brand imitations of luxury products are distributed, the more positive consumer evaluations. In the case of convenience goods however, the impact of store image on consumer evaluations depends on the presence or absence of the imitated brand. Four consumer characteristics were found to correlate negatively with evaluations of brand imitations: product category involvement, product familiarity, brand sensitivity and generalised brand loyalty.

Journal Article Are Children Decision‐Makers within the Household? Get access Anyck Dauphin, Anyck Dauphin Université du Québec en Outaouais, CIRPÉE Search for other works by this author on: Oxford Academic Google Scholar Abdel‐Rahmen El Lahga, Abdel‐Rahmen El Lahga Institut Supérieur de Gestion de Tunis Search for other works by this author on: Oxford Academic Google Scholar Bernard Fortin, Bernard Fortin Université Laval, CIRPÉE, CIRANO Search for other works by this author on: Oxford Academic Google Scholar Guy Lacroix Guy Lacroix Université Laval, CIRPÉE, CIRANO Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 121, Issue 553, 1 June 2011, Pages 871–903, https://doi.org/10.1111/j.1468-0297.2010.02404.x Published: 21 January 2011 Article history Received: 22 September 2008 Accepted: 26 July 2010 Published: 21 January 2011

The coefficient of variation (CV) is a quality characteristic that has several applications in applied statistics and is receiving increasing attention in quality control. Few papers have proposed control charts that monitor this normalized measure of dispersion. In this paper, an adaptive Shewhart control chart implementing a variable sampling interval (VSI) strategy is proposed to monitor the CV. Tables are provided for the statistical properties of the VSI CV chart, and a comparison is performed with a Fixed Sampling Rate Shewhart chart for the CV. An example illustrates the use of these charts on real data gathered from a casting process. Copyright © 2012 John Wiley & Sons, Ltd.

Cloud computing technology is a relatively new concept of providing scalable and virtualized resources, software and hardware on demand to consumers. It presents a new technology to deliver computing resources as a service. It offers a variety of benefits like services on demand and provisioning and suffers from several weaknesses. In fact, security presents a major obstacle in cloud computing adoption. In this paper, the authors will deal with security problems in cloud computing systems and show how to solve these problems using a quantitative security risk assessment model named Multi-dimensional Mean Failure Cost (M2FC). In fact, they summarize first security issues related to cloud computing environments and then propose a generic framework that analysis and evaluate cloud security problems and then propose appropriate countermeasures to solve these problems.

Purpose This paper aims to develop a model that will include self‐image congruence, attitudes, satisfaction and explain their role on behavioural intentions. Design/methodology/approach A qualitative survey was carried out. It was followed by a quantitative study, conducted in two stages to clarify the self‐congruity scale. Results were used to lead an exploratory investigation of a conceptual model of behavioural intentions. Findings Ideal self‐image congruity had a stronger direct effect on attitudes, than did actual self‐image congruity. Indeed, there was evidence in this study that behavioural intentions were directly or indirectly influenced by shopper's self‐congruity, attitudes, and satisfaction. Research limitations/implications No research has ever simultaneously compared the relative influence of these important constructs on service encounter outcomes. The obvious implication is the need for further consideration of similar composite models. Indeed, it would be instructive in future research to assess the extent to which individual characteristics and demographic variables mediate self‐congruity, attitudes and satisfaction, and their influence on behavioural intentions. Practical implications Retailers need to realize that positioning their store to enhance the likelihood of self‐congruity with target shoppers and improve their attitudes, is important but not enough. The goal for improving self‐congruity and attitudes is to make sure shoppers have received the benefit they expect to find. The benefits received directly influence shoppers' decisions on whether they will want to come back in the future and what they will say about the store to others. Originality/value This research provides an improved understanding of the role of self‐congruity, attitudes and satisfaction on behavioural intentions. The inclusion of self‐congruity and attitudes in the proposed model as antecedents of behavioural intentions is one of the unique aspects of the study.

The stock market is viewed as an unpredictable, volatile, and competitive market. The prediction of stock prices has been a challenging task for many years. In fact, many analysts are highly interested in the research area of stock price prediction. Various forecasting methods can be categorized into linear and non-linear algorithms. In this paper, we offer an overview of the use of deep learning networks for the Indian National Stock Exchange time series analysis and prediction. The networks used are Recurrent Neural Network, Long Short-Term Memory Network, and Convolutional Neural Network to predict future trends of NIFTY 50 stock prices. Comparative analysis is done using different evaluation metrics. These analysis led us to identify the impact of feature selection process and hyper-parameter optimization on prediction quality and metrics used in the prediction of stock market performance and prices. The performance of the models was quantified using MSE metric. These errors in the LSTM model are found to be lower compared to RNN and CNN models.

Purpose The present paper aims to propose a measurement scale of the customer knowledge enabled innovation (CKEI) capability. Design/methodology/approach Data were generated on the basis of in‐depth interviews with managers and was analysed using the “Sphinx” software. Findings A pool of 60 items is proposed for the eventual measurement of the CKEI capability. Research limitations/implications The proposed measurement scale is still raw. Further research is needed in order to test its psychometric features as well as to validate its relevance into a general model of customer knowledge management and innovation. Practical implications Organizations need to measure the degree to which they are endowed with the capability of managing effectively customer knowledge in order to foster innovation. The CKEI scale is considered as a barometer allowing organizations to evaluate to what extent they are endowed with the capacity of co‐creating value with their customers. The proposed CKEI scale is believed to provide managers with the opportunity to regularly monitor their innovative capability and be close to their customers. Originality/value The CKEI capability is proposed as a new construct focusing on integrating both innovation and knowledge‐based capabilities literatures. The CKEI measurement scale is believed to extend these literatures and to add knowledge to these areas of research..

Warehouse order picking activities are among the ones that impact the most the bottom lines of warehouses. They are known to often account for more than half of the total warehousing costs. New practices and innovations generate new challenges for managers and open new research avenues. Many practical constraints arising in real-life have often been neglected in the scientific literature. We introduce, model and solve a rich order picking problem under weight, fragility and category constraints, motivated by our observation of a real-life application arising in the grocery retail industry. This difficult warehousing problem combines complex picking and routing decisions under the objective of minimising the distance travelled. We first provide a full description of the warehouse design which enables us to algebraically compute the distances between all pairs of products. We then propose two distinct mathematical models to formulate the problem. We develop five heuristic methods, including extensions of the classical largest gap, mid-point, S-shape and combined heuristics. The fifth one is an implementation of the powerful adaptive large neighbourhood search algorithm specifically designed for the problem at hand. We then implement a branch-and-cut algorithm and cutting planes to solve the two formulations. The performance of the proposed solution methods is assessed on a newly generated and realistic test bed containing up to 100 pickups and 7 aisles. We compare the bounds provided by the two formulations. Our in-depth analysis shows which formulation tends to perform better. Extensive computational experiments confirm the efficiency of the ALNS metaheuristic and derive some important insights for managing order picking in this kind of warehouses.

Governments around the world have responded to the COVID-19 outbreak with a mix of policies. The strictest responses of the New Zealand government are notable, given their abilities to contain and limit the spread of the virus. However, their impacts on stock returns remain unclear. This paper investigates the impact of three policies, namely lockdown, the stimulus package, and the travel ban, on the returns of 14 New Zealand industry stock indices. Using daily data from 1 January 2019 to 25 August 2020, evidence points to a heightened level of integration among the various industry stock indices during the early stages of the pandemic. Only lockdown has had a positive impact on aggregate stock returns, suggesting its ability to raise investors’ confidence in the overall stock market. At the industry level, the impact of the three response policies is generally positive but heterogeneous across industry stock indices. Notably, none of the three adopted policies significantly impact technology, healthcare, and real estate returns.

While prior studies have investigated the impact of corporate governance mechanisms on corporate social responsibility (CSR) commitment, researchers have scantly studied the potentially important relationship between board gender diversity and corporate social responsibility and irresponsibilityseparately. Drawing on the social role theory and feminist ethics, we hypothesizethat board gender diversity is positively associated with CSR and negatively associated with corporate social irresponsibility (CSI).Here, we relied on a sample of French non-financial companies listed on the SBF 120 index between 2011 and 2016. Our results provide evidence on the positive impact of board gender diversity on CSR and the negative one on CSI. We show that women have a stronger impact on reducing CSI than on enhancing CSR. Our findings were robust to the different estimation methods.

The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.