Relationship between accounting benefits and ERP user satisfaction in the context of the fourth industrial revolution

The importance of corporate social responsibility is shaping investment decisions and entrepreneurial actions in diverse perspectives. The rapid growth of SMEs has tremendous impacts on the environment. Nonetheless, the economic emergence plan of Cameroon has prompted government support of SMEs through diverse projects. This saw economic growth increased to 3.8% and unemployment dropped to 4.3% caused by the expansion of private sector investments. The dilemma that necessitated this study is the response strategy of SMEs operators towards environmental sustainability. This study, thus seeks to examine the effects of entrepreneurial intentions and actions on environmental sustainability. The research is a conclusive case study design supported by the philosophical underpins of objectivism ontology and positivism epistemology. Data was sourced from four hundred (400) SMEs operators purposively sampled from the Centre and Littoral regions of Cameroon using structured questionnaire. Data was analysed using the Structural Equation Modelling technique with the aid of statistical packages including: SPSS 24 and AMOS 23. The study revealed that entrepreneurial action has weak positive statistical significant impacts on environmental sustainability; whereas entrepreneurial intention has strong positive statistical significant effects on environmental sustainability. Entrepreneurial intention comprised of self-efficacy and perceived control whereas, entrepreneurial actions involved entrepreneurial alertness and uncertainty. This study concludes that entrepreneurs in Cameroon have sustainable intentions to protect the environment but; the current actions taken are inadequate. This research recommends that entrepreneurs should enhance efforts toward attaining the state of genuine sustainabilit

Solution Repair/Recovery in Uncertain Optimization Environment

Operation management problems (such as Production Planning and Scheduling) are represented and formulated as optimization models. The resolution of such optimization models leads to solutions which have to be operated in an organization. However, the conditions under which the optimal solution is obtained rarely correspond exactly to the conditions under which the solution will be operated in the organization.Therefore, in most practical contexts, the computed optimal solution is not anymore optimal under the conditions in which it is operated. Indeed, it can be "far from optimal" or even not feasible. For different reasons, we hadn't the possibility to completely re-optimize the existing solution or plan. As a consequence, it is necessary to look for "repair solutions", i.e., solutions that have a good behavior with respect to possible scenarios, or with respect to uncertainty of the parameters of the model. To tackle the problem, the computed solution should be such that it is possible to "repair" it through a local re-optimization guided by the user or through a limited change aiming at minimizing the impact of taking into consideration the scenarios

Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems

Information systems and healthcare XXIV: Factors affecting the EAI adoption in the healthcare sector

Recent developments in the field of integration technologies like Enterprise Application Integration (EAI) have emerged to support organizations towards improving the quality of services and reducing integration costs. Despite the importance of EAI, there is limited empirical research reported on its adoption in the healthcare sector. Khoumbati et al. [2006] developed a model for the evaluation of EAI in healthcare organizations. In doing so, the causal interrelationship of EAI adoption factors was identified by using fuzzy cognitive mapping. This paper is a progression of previous work in the area and seeks to contribute by validating the model through a different case environment. Thus, this paper contributes by deriving and proposing the MAESTRO model for EAI adoption. MAESTRO identifies a set of factors that influence EAI adoption and it is evaluated through a real-life case study. It provides an understanding of the EAI adoption process through its grounding on empirical data. In doing so, the MAESTRO model supports the management of healthcare organizations during the decision-making process for EAI adoption

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Abstract State Machines 1988-1998: Commented ASM Bibliography

An annotated bibliography of papers which deal with or use Abstract State Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm

Equations for predicting airborne cleanliness in non-unidirectional airflow cleanrooms

Equations are derived in this paper for predicting the airborne concentration of particles and microbe-carrying particles in non-unidirectional airflow cleanrooms during manufacturing. The equations are obtained for a variety of ventilation systems with different configurations for mixing fresh and recirculated air, air filter placements, and number and efficiency of air filters. The variables in the equations are air supply rate, airborne dispersion rate of contamination from machinery and people, surface deposition of particles from air, particle concentration in fresh makeup air, proportion of make-up air, and air filter efficiencies. The equations are amenable to relatively simple modification for the study of different cleanroom ventilation systems. The use of these equations to investigate the effect of different configurations of ventilation systems and the relative importance of the equation variables on airborne concentrations will be reported in a further paper