How intentions to create a social venture are formed. A case study

This exploratory study on one social entrepreneur challenges existing knowledge on the intention formation process of entrepreneurship. Drawing from social and cognitive psychology, we adapt an intention-based model from entrepreneurship and translate it to social entrepreneurship. Building on our findings, we argue that social entrepreneurs - like traditional entrepreneurs - experience perceptions of feasibility and desirability, and a propensity to act. However, complementing research on traditional entrepreneurs, we suggest that, in a preceding stage, social entrepreneurs develop social sentiments. Furthermore, we identify willpower, support, and the construction of opportunity as important antecedents of perceptions of feasibility and desirability, and propensity to act.social entrepreneurship; intention; cognition;

Modelling urban floods using a finite element staggered scheme with porosity and anisotropic resistance

Artificial porosity models for urban flooding use porosity as a statistical descriptor for the presence of buildings, which are then treated as subgridscale features. Computational efficiency makes porosity models attractive for large-scale applications. These models are typically implemented in the framework of two-dimensional (2D) finite volume collocated schemes. The most effective schemes, falling under the category of Integral Porosity models, allow accounting for a wealth of sub-grid processes, but they are known to suffer from oversensitivity to mesh design in the case of anisotropic porosity fields. In the present exploratory study, a dual porosity approach is implemented into a staggered finite element numerical model. The free surface elevation is defined at grid nodes, where continuity equation is solved; fluxes are conveyed by triangular cells, which act as 2D-links between adjacent grid nodes. The presence of building is modelled using an isotropic porosity in the continuity equation to account for the reduced water storage, and an anisotropic conveyance porosity in the momentum equations to compute bottom shear stress. Both porosities are defined on an element-by-element basis, thus avoiding mesh-dependency. Although suffering a number of limitations, the model shows promising results

The mediation between participative leadership and employee exploratory innovation: Examining intermediate knowledge mechanisms

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.We examine mediation effects of coworker knowledge sharing and absorptive capacity on the participative leadership–employee exploratory innovation relationship in R&D units of Taiwanese technology firms. Deploying a time-lagged questionnaire method implemented over four business quarters, data is generated from 1600 paired samples (managers and employees) in R&D units of Taiwanese technology firms. The structural equation modeling results reveal that (1) participative leadership is positively related to employee exploratory innovation; (2) coworker knowledge and (3) absorptive capacity partially mediate the relationship between participative leadership and employee exploratory innovation independently; and, (4) coworker knowledge sharing in combination with absorptive capacity partially mediates this relationship. The results extend previous research on participative leadership and innovation by demonstrating that participative leadership is related to employee exploratory innovation (Lee and Meyer-Doyle, 2017; Mom et al., 2009).Results also confirm that participative leadership drives employee exploratory innovation through employee absorptive capacity. This reinforces the need highlighted by Lane et al. (2006) to investigate the role of absorptive capacity at the individual-level. Collectively, while participative leadership is important for employee exploratory innovation it is the knowledge mechanisms existing and interacting at the employee-level that are central to generating increased employee exploratory innovation from this leadership approach

Neural Network-Ant Colony Optimization Model of Residential Building Project Cost: Exploratory Approach

Neural network and ant-colony are two important tools that could be used to provide solution in situation of multivariate environment that requires pareto optima solutions. In this study therefore, combination of neural network and ant colony method was used to generate an optimization cost model. Neural network is a conventional method currently being used in cost modeling, given its advantage over traditional regression method. It is based on this, that this study used the combination of neural network and regression method to model cost of residential building projects. One hundred and fifty (150) samples of residential building projects were selected at random and divided into two; one part is used in developing network algorithm for neural network and ant colony, while the second part is used for model validation. Neural network is used to generate which was divided into modules: the data optimization module, criteria selection with initializing and terminating modules. Regression analysis was carried out and model validated with Jackknife re-sampling technique and previously developed ant colony model (MOACO, MOTACO and MAWA). The co-linearity analysis indicates high level of tolerance and -0.0756 lowest variation prediction quotients to 0.8678 highest variation quotients. Also the Regression coefficient (R-square) value for determining the model fitness is 0.069 with standard error of 0.045. These results attests to the fitness of the model generated. The model is flexible in accommodating new data and variables, thus, it allows for continuous updating. Keywords: Expert system, Co-linearity, Informatics, Residential-Building

Hybrid artificial bee colony algorithm with branch and bound for two–sided assembly line balancing

The two-sided assembly-line balancing (2SALB) is widely used in various production systems especially in high-volume large-size products. However, not many types of research are focused on the study of the 2SALB problem. Recently, the artificial bee colony (ABC) algorithm was used in the solution process where it was considered as a very useful, effective and well-known algorithm. Nevertheless, the ABC is also known to be a slow converging method in achieving an optimal solution. This research is intended to improve the ABC performance in solving the 2SALB problem with the objectives to hybrid ABC algorithm with branch and bound concept and to evaluate the performance of this algorithm in minimizing idle time and number of the workstation . The two-sided assembly line data was tested in modified spreadsheet model which is equipped with random priority rules in order to get multi-solution of ABC approach for balancing two-sided assembly line. The feasible number of workstations was determined with the minimum idle time of every mated station and maintains the minimum one in each cycle. This was done by calculating the partial fitness of the mated station. The branch and bound concept was applied by using mated workstations to overcome the slow convergence of the ABC algorithm and solve the problem optimality. The visual basic application software was used to generate different solutions with the various partial fitness of the proposed approach. The modified ABC algorithm was tested with the 2SALB problems involving P9, P12, P16, P24, P44 and P65 tasks. The results were tested by comparing hybrid ABC with pure ABC, where it was found that hybrid ABC had less number of workstation with minimum partial fitness value. In addition, the comparison was done with other research on ABC with full constraints and the proposed approach shows improvement by reduction of two workstations in sixteen task problem and reduction of one workstation at nine, twenty four and sixty five task problem sizes. As a conclusion, the proposed hybrid of ABC with Branch and Bound concept has increased the effectiveness of 2SALB solutions through the evaluation of many alternative mated stations results before going to assign to next mated stations to obtain minimum workstations with given cycle time solution

Failure is an option:an innovative engineering curriculum

PurposeAdvancements and innovation in engineering design are based on learning from previous failures but students are encouraged to ‘succeed’ first time and hence can avoid learning from failure in practice. The purpose of the study was to design and evaluate a curriculum to help engineering design students to learn from failure.Design/Methodology/ApproachA new curriculum design provided a case study for evaluating the effects of incorporating learning from failure within a civil engineering course. An analysis of the changes in course output was undertaken in relation to graduate destination data covering 2006 to 2016 and student satisfaction from 2012 to 2017 and a number of challenges and solutions for curriculum designers were identified.FindingsThe design and delivery of an innovative curriculum, within typical constraints, can provide opportunities for students to develop resilience to failure as an integral part of their learning in order to think creatively and develop novel engineering solutions. The key issues identified were: the selection of appropriate teaching methods, creating an environment for exploratory learning, group and team assessments with competitive elements where practicable, and providing students with many different pedagogical approaches to produce a quality learning experience.OriginalityThis case study demonstrates how to design and implement an innovative curriculum that can produce positive benefits of learning from failure. This model can be applied to other disciplines such as building surveying and construction management. This approach underpins the development of skills necessary in the educational experience to develop as a professional building pathologist