A. D. Amar

Motivating knowledge workers to innovate: a model integrating motivation dynamics and antecedents

Grounding in the premise that motivation in a knowledge work environment is an outcome of the existence of certain antecedents that are responsive to the dynamics of motivation in this environment, this work develops a conceptual model synthesizing motivations “what” and “how” that will bring human creativity in organizations which thrive on innovation. The presented model embodies these dynamics into five drivers of motivation practice in the knowledge work environment and then leads to three sources of motivation into which its antecedents should be loaded to motivate knowledge employees for innovation.

Knowledge management, strategy, and technology: a global snapshot

Purpose – This paper presents results from a large comprehensive global survey of knowledge management (KM). The question that this paper looks to answer is how a KM strategy can be developed that will enable its successful management in organisations of many sectors and sizes. In light of the observed developments since the previous knowledge management surveys, the purpose of this paper is to redefine the KM function, and how it should be performed.Design/methodology/approach – The research instrument was a global online hosted survey answered by nearly 700 participants. Hypotheses on the KM management process in contemporary organisations, emphasising the importance of strategy, cover this work. These hypotheses are tested by a set of questions and supported by the survey responses.Findings – It is demonstrated that in many organisations technology has failed to have much impact on the way knowledge is transferred and shared.Research limitations/implications – It should be noted that no control was mad...

A descriptive model of innovation and creativity in organizations: a synthesis of research and practice

Based on a review of the reported theoretical advances that are affirmed by empirical works from published management practice, this paper finds that innovation is an outcome of an intentional and designed effort of the organization, and synthesizes the findings for enhancing creativity and innovation by developing three antecedents that positively influence innovation in organizations: (1) the garnered knowledge, (2) the presence of a knowledge-supporting culture, and (3) the accumulation of social capital. The paper also provides a summary of transferable management practices that organizations are employing to enhance innovation and productivity.

Simulated Versus Real Life Data in Testing the Efficiency of Scheduling Algorithms

Abstract This paper compares and contrasts the use of simulated versus real data in testing the efficiency of scheduling algorithms. Five hypotheses, formulated to evaluate two important measures of algorithm efficiency, viz., CPU time and the number of iterations, are tested on over one hundred problems drawn from different sources. On the basis of these empirical results, it is shown that: (1) the real problems are easier to solve than the simulated ones; and (2) the Natural Order heuristic is more effective on real problems than simulated ones. Implications of these results for testing the efficiency of algorithms are discussed.

Leading for innovation through symbiosis

Based on the experiences with various organizations, this article summarized how managers should lead their innovation employees for most effectiveness. The main thesis is that innovation employees give higher performance when their managers practice symbiotic leadership style. The article describes how managers can achieve this. Four characteristics that they can apply to guide their leadership style to create a work symbiosis are given. Another contribution of this article is a set of recommendations to mimic the aspects of symbiotic leadership applicable in any organization where the operating environment is uncertain, the task is unstructured, and the manager cannot provide a clear direction to his employees. Through the practice of these recommendations, managers of all types of organizations should get higher productivity from their employees due to the employees’ individual innovations.

An Empirical Evaluation of the “Entrapment” Procedure for Scheduling Jobs on Identical Machines

Abstract This work empirically evaluates the “entrapment” procedure used by Barnes and Brennans branch-and-bound algorithm to minimize total cost of tardy jobs to be processed on identical machines. Using 25 problems taken from various known sources, it is shown that entrapment does not necessarily decrease computational time required to optimally solve such a problem.

SCHEDULING ON A BOTTLENECK STATION : A COMPREHENSIVE COST MODEL AND HEURISTIC ALGORITHMS

This paper presents a mathematical model for scheduling a single processor system to minimize aggregate operations cost consisting of inventory holding cost, opportunity cost due to the delay in realization of revenue, inflationary spiral effect, and the time value of money on costs yet to be incurred. We propose a number of heuristic algorithms for solving the model and provide extensive numerical experiments. Computational results are reported.

Designing and Operating Communities of Practice for Managing Knowledge: Lessons from a Comprehensive Global Knowledge Management Survey

Based on a survey of 1,034 knowledge managers, knowledge workers, and senior managers from organization of all types, forms, and sizes from many industrial sectors from 76 countries around the world, from July 17, 2007, to November 14, 2011, we study the role of communities of practice (CoPs) in managing knowledge in organizations. We formulate 10 hypotheses and draft 51 questions to test these hypotheses pertaining to forming and operating CoPs. We find that properly managed CoPs could perform many essential tasks in creating, sharing, and applying knowledge to operations for change and innovation. The lessons drawn help us provide guidelines for designing and operating communities of practice (CoPs) in organizations.

A multi-neural-network learning for lot sizing and sequencing on a flow-shop

This work investigates the use of neural networks to schedule flow shops with dynamic job arrival. The objective of such scheduling is to determine both sequence and lot-sizes concurrently that minimize a makespan. We present a multi-neural network architecture for simultaneous lot-sizing and sequencing neural-network learning. Our experimental study shows that our multi-neural networks can learn both lot-sizing and sequencing decisions, and outperform a sequencing-only neural network.

Leadership for knowledge organizations

Purpose – The purpose of this paper is to give some theoretical foundation to leadership function and style for managing knowledge workers whose work, by definition, is non-routine, thrives on innovation, and places a special demand on autonomy for its execution. Design/methodology/approach – Extant search of literature to look for evidence supporting successful leadership theories and practices that are shown to improve performance of knowledge workers. Synthesis of findings to structure a framework in the form of major propositions for their testing by future research. Findings – The authors begin with establishing the first finding that states that leadership of knowledge organizations is different from the leadership of traditional organizations. Then the authors build six additional findings for shaping a successful leadership process for knowledge organizations. Research limitations/implications – Since it is a theoretical paper built on a search of literature in the field of leadership, there is a ...