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Conceptual Framework of How Rewards Facilitate Business Operations in Knowledge-Intensive Organizations

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System Performance and Management Analytics

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Abstract

Knowledge workers contribute to operations of their employers through their skills and expertise to solve complex business problems. This makes then indispensable and crucial to the success of such firms.

Following the framework developed by Tsui et al. (Academy of Management 40(5):1089–1121, [12]), this paper addresses the debate regarding the effectiveness of intrinsic and extrinsic rewards in providing a climate of satisfaction in knowledge-intensive organizations. It creates propositions about the how the perception of knowledge workers changes in response to changes in the intrinsic and extrinsic components of rewards. Keeping employment relationships as backdrop and inherent work-related needs of knowledge workers into consideration, this paper makes propositions regarding the possible impact of changes in intrinsic and extrinsic rewards on ‘α’ or the perceived value of employment in unbalanced relationships. Knowledge workers are known to be more driven by characteristics of their work than extrinsic aspects of rewards. Therefore, the paper suggests that the share of intrinsic rewards must be either more or (at least) equal to that of extrinsic rewards.

This paper adds to the long drawn debate between the effectiveness of intrinsic and extrinsic rewards. It draws attention to the need for organizations to focus on the existing employment relationship and the work-related needs of knowledge workers while taking reward decisions.

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Notes

  1. 1.

    Impact in the short term and the long term refers to the increase or decrease in α in comparison to the initial value of α; before the change in reward combination.

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Correspondence to Shikha Kapoor .

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Shrivastava, S., Kapoor, S. (2019). Conceptual Framework of How Rewards Facilitate Business Operations in Knowledge-Intensive Organizations. In: Kapur, P., Klochkov, Y., Verma, A., Singh, G. (eds) System Performance and Management Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7323-6_30

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