Despite the vast amount of research focusing on intrinsic and extrinsic motivation, the effects of extrinsic motivators on creativity and innovation have been scarcely investigated. Extrinsic factors can be seen as synergistic extrinsic motivators when they have a positive effect on the outcome. The present study investigates synergistic extrinsic motivators that organizations can use to foster creativity and innovation of their intrinsically motivated knowledge workers. The analysis is based on Amabile and Pratt’s dynamic componential model of creativity and innovation in organizations combined with elements from Ryan and Deci’s self-determination theory. The quantitative data stemmed from 90 knowledge workers of an international consulting company who participated in an online self-assessment. In exploratory factor analyses, extrinsic motivation items consolidated two factors “relational rewards” and “transactional rewards”, while creativity and innovation items resulted in a one-factor solution, called “creativity/ innovation performance”. The results of hierarchical regression analyses confirmed the widely found positive effects of intrinsic motivation on creative and innovative performance. Moreover, the results supported the hypothesis that the extrinsic motivator, relational rewards, moderated the relationship between intrinsic motivation and creativity/ innovation performance significantly and positively. The findings showed the higher the perceived probability of receiving relational rewards and the higher the intrinsic motivation, the greater the positive effect on creative/ innovative outcomes. At the same time, the results did not confirm the hypothesis, that the moderator transactional rewards had a statistically significant effect on the relationship between intrinsic motivation and creative/ innovative performance. Finally, the empirical evidence provided practical implications on how to stimulate the creativity/ innovation performance of knowledge workers within organizations.

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The Influence of Intrinsic Motivation and Synergistic Extrinsic Motivators on Creativity and Innovation.

Evolutionary food quality and location strategies for restaurants in competitive online-to-offline food ordering and delivery markets: An agent-based approach

Evolutionary location and pricing strategies for service merchants in competitive O2O markets

In most practical problems of linear programming problems with fuzzy cost coefficients, some or all variables are restricted to lie within lower and upper bounds. In this paper, the authors propose a new method for solving such problems called the bounded fuzzy primal simplex algorithm. Some researchers used the linear programming problem with fuzzy cost coefficients as an auxiliary problem for solving linear programming with fuzzy variables, but their method is not efficient when the decision variables are bounded variables in the auxiliary problem. In this paper the authors introduce an efficient approach to overcome this shortcoming. The bounded fuzzy primal simplex algorithm starts with a primal feasible basis and moves towards attaining primal optimality while maintaining primal feasibility throughout. This algorithm will be useful for sensitivity analysis using primal simplex tableaus.

Bounded Primal Simplex Algorithm for Bounded Linear Programming with Fuzzy Cost Coefficients

Given the increasingly significant impact of an efficient product-location strategy on warehouses' performance from a service level and operational costs perspective, this paper presents a possible operations research-oriented solution to provide a tangible reduction of the overall required warehousing space, thereby translating the storage location assignment problem (SLAP) into a vertex colouring problem (VCP). Developing the topic of their previous work on the development of an effective multi-product slot- code optimization heuristic, the authors focused on finding a cost-effective way to solve the SLAP through a mathematical-optimization approach. The formulation validation on a real industrial case showed its high optimization potential, and benchmarking simulations displayed performances significantly close to the best theoretical case. Indeed, the optimized value results were definitively close to the SLAP lower bound calculated assuming a randomized storage policy which, distinct from the developed solution, must inevitably be supported by warehouse management system software. On the contrary, the proposed methodology relies upon a dedicated storage policy, which is easily implementable by companies of all sizes without the need for investing in expensive IT tools.

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Minimizing Warehouse Space with a Dedicated Storage Policy

This paper presents a model to support the banking managerial decisions in the evaluation of investment plans, especially on rejecting inappropriate plans that can be done in short time (less than hour) and with minimal cost. Because there are some uncertainties in the evaluation process, our proposed model utilises fuzzy set theory to define the problem space in which an acceptance or rejection decision for a submitted investment plan is made. The model is based on lessons-learned concept and developed through the combination of case-based reasoning (CBR) and multiple attribute decision making in fuzzy environment. The model uses an enhanced version of CBR in which a novel concept as solution's truth value is implemented. A set of investment plans is evaluated to show the applicability and efficiency of the model. Different scenarios in terms of sensitivity analysis are also mentioned to capture managerial insights. Comparing the obtained results of the model with those of other algorithms shows its better proximity to human reasoning and decision making.

A hybrid algorithm of improved case-based reasoning and multi-attribute decision making in fuzzy environment for investment loan evaluation

In this paper, a novel fuzzy MADM model with some specifications that make it distinguished from the available methods. Decision matrix is defined as a full fuzzy structure. The model only uses information on the alternatives i.e. does not require pre-assigned weight values for the attributes. The weights will be achieved through a matching mechanism between the obtainable alternatives' preferences and a fuzzy preference relation of the alternatives stated by decision makers. This is applied using a mathematical programming model. Since the resulted model is non-linear and hard to solve using the classic optimization methods, Simulated Annealing is proposed to find optimum or near optimum weights. Having the weights of attributes, the alternatives' ranking is determined using the statistical measures of their fuzzy values. The model is applied on the project selection problem to study its efficiency and applicability to MADM problems.

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A New Fuzzy MADM Approach and its Application to Project Selection Problem

The graph colouring problem, as an important NP-complete problem, is considered in this paper and a hybrid meta-heuristic approach is developed to solve it The initial solution of the algorithm, generated by a heuristic method, is used by a simulated annealing (SA) approach to generate new solutions until no progress in a number of solutions reported At this stage, the algorithm will use a tabu search routine and this local search operator will be followed for some iterations After finding a better solution, the algorithm is again followed through SA Efficiency of the algorithm is showed through various experiments on well-known benchmark problems of DIMACS Comparison with the available algorithms shows considerable performance in terms of solution quality and computational time

A hybrid algorithm of simulated annealing and tabu search for graph colouring problem

This paper develops a model for facility location optimisation of an entrant firm in a competitive environment. Customers have elastic demand and behave probabilistically based on a utility function that depends on three factors, price, travelling time and waiting time at facilities. However, they do not know the exact waiting times when they are at their origins and plan to use the new or redesigned network for the first time. They amend their next decisions based on their observations on waiting times. The resulting non-linear model is solved using Tabu Search. A set of experiments is performed to show the algorithm’s efficiency.

A competitive facility location model with elastic demand and patronising behaviour sensitive to location, price and waiting time

This paper reviews classic approaches for modeling customers’ choice behavior in competitive facility planning problems. They are either deterministic or probabilistic and work by a utility function based on some factors whether customer-independent or dependent. This paper focuses especially on congestion, the most important factor in customer to service or fixed-server systems. Various behaviors which customers may divulge when they face with a congested facility are extensively studied. We also define a new congestion-sensitivity reaction which has not been considered in the literature. Relevant modeling approaches are proposed to formulate customers-sensitivity to congestion. An illustrative example is also given to analyze and compare the proposed approaches.

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Ali Pahlavani

Papers

A hybrid algorithm of improved case-based reasoning and multi-attribute decision making in fuzzy environment for investment loan evaluation

A New Fuzzy MADM Approach and its Application to Project Selection Problem

A hybrid algorithm of simulated annealing and tabu search for graph colouring problem

A competitive facility location model with elastic demand and patronising behaviour sensitive to location, price and waiting time

Modeling Customer Reactions to Congestion in Competitive Service Facilities