Utility-Based Knowledge Work Productivity Assessment

2014 ◽  
Vol 2 (2) ◽  
pp. 28-46 ◽  
Author(s):  
M. Xiao ◽  
D.A. Nembhard
Keyword(s):  
Knowledge Worker ◽  
Work Productivity ◽  
Knowledge Work ◽  
Assessment Model ◽  
Assessment Process ◽  
Risk Attitudes ◽  
Worker Productivity ◽  
Worker Performance ◽  
Different Levels ◽  
Productivity Assessment

This paper presents a utility-based productivity assessment model for evaluating knowledge worker productivity, with the goal of examining the assessment process for knowledge workers with varying levels of knowledge intensity. The authors conduct an experiment to discover effects from knowledge intensity on managerial assessments of knowledge worker performance. The model presented allows for the quantification of evaluator's risk attitudes and preference, as well as relative weights for three chosen productivity metrics. The results indicate that managers' risk attitudes vary with respect to both different metrics, and to different levels of knowledge intensity.

A Survey of Knowledge Work Productivity Metrics

2012 ◽  
Vol 1 (2) ◽  
pp. 1-18 ◽  
Author(s):  
Min Xiao ◽  
David A. Nembhard ◽  
Changjun Dai
Keyword(s):  
High Intensity ◽  
Knowledge Worker ◽  
Work Productivity ◽  
Knowledge Work ◽  
Intensity Level ◽  
Work Intensity ◽  
Worker Productivity ◽  
Work Effectiveness ◽  
Group Interviews ◽  
Depth Interviews

This paper presents a unique comparison of work on productivity metrics in the literature and that in use in practice, with the aim of identifying gaps, and opportunities for researchers and practitioners to meet the challenge of improving knowledge worker productivity. Methods used include surveys, group interviews, and in-depth interviews. The authors conclude that several metrics including effectiveness, efficiency, profitability, innovation, and customer satisfaction may need to be given more attention when considering productivity evaluation. It is also important to identify knowledge work intensity, and select metrics that are most appropriate for each worker’s knowledge intensity level. Results provide insights for enterprises to identify useful metrics for evaluating the knowledge workforce. Specifically, for high intensity work, effectiveness is a valuable metric, but for lower intensities, efficiency may be more practical.

The Relation of Knowledge Intensity to Productivity Assessment Preferences and Cultural Differences

2017 ◽  
pp. 1565-1583
Author(s):  
David Nembhard ◽  
Min Xiao
Keyword(s):  
Cultural Differences ◽  
Work Productivity ◽  
Knowledge Work ◽  
The Other ◽  
Intensity Level ◽  
Evaluation Tool ◽  
The Us ◽  
High Knowledge ◽  
Measures Of Performance ◽  
Productivity Assessment

The authors investigate commonalities and differences in productivity assessment preferences among managers from two different cultural settings, one in the US and the other in China. They also investigate these differences for two knowledge intensity levels to inform how the type of work being assessed affects these preferences. The results illustrate significant differences among the two organizations, among the knowledge intensity levels, and that these results are generally dependent on the specific measures of performance being evaluated. The US organization's managers tended to view quality as the most important metric for the high knowledge intensity work, and customer satisfaction as the most important metric for the low knowledge intensity work. The Chinese managers viewed innovation as the most important metric for the high knowledge intensity level jobs, and quality as the most important metric for the low knowledge intensity level jobs. These results indicate that utility-based work productivity model can be used as an evaluation tool to measure knowledge work productivity.

The Relation of Knowledge Intensity to Productivity Assessment Preferences and Cultural Differences

2017 ◽  
Vol 5 (1) ◽  
pp. 1-19
Author(s):  
David Nembhard ◽  
Min Xiao
Keyword(s):  
Cultural Differences ◽  
Work Productivity ◽  
Knowledge Work ◽  
The Other ◽  
Intensity Level ◽  
Evaluation Tool ◽  
The Us ◽  
High Knowledge ◽  
Measures Of Performance ◽  
Productivity Assessment

The authors investigate commonalities and differences in productivity assessment preferences among managers from two different cultural settings, one in the US and the other in China. They also investigate these differences for two knowledge intensity levels to inform how the type of work being assessed affects these preferences. The results illustrate significant differences among the two organizations, among the knowledge intensity levels, and that these results are generally dependent on the specific measures of performance being evaluated. The US organization's managers tended to view quality as the most important metric for the high knowledge intensity work, and customer satisfaction as the most important metric for the low knowledge intensity work. The Chinese managers viewed innovation as the most important metric for the high knowledge intensity level jobs, and quality as the most important metric for the low knowledge intensity level jobs. These results indicate that utility-based work productivity model can be used as an evaluation tool to measure knowledge work productivity.

Knowledge Worker Performance in a Cross-Industrial Perspective

2012 ◽  
pp. 297-321
Author(s):  
Rainer Erne
Keyword(s):  
Performance Indicator ◽  
Knowledge Workers ◽  
Knowledge Worker ◽  
Knowledge Work ◽  
Oecd Countries ◽  
Alternative Definition ◽  
Worker Performance ◽  
Knowledge Intensive ◽  
Definition Of ◽  
Business Sectors

Knowledge workers in specific professional domains form the fastest increasing workforce in OECD countries. Since this fact has been realised by management researchers, they have focussed on the question of how to measure and enhance the productivity of said workforce. According to the author’s cross-industrial research undertaken in five different knowledge-intensive organisations, it is, however, not productivity in the traditional meaning of the term which is to be regarded as the crucial performance indicator in knowledge work. There rather exist multiple performance indicators, each of which is, moreover, differently graded as to its importance by different stakeholders. These findings, firstly, indicate the need for an alternative definition of productivity when the term is applied to knowledge work. Secondly, they indicate the need for alternative definitions of the specific challenges that might be involved in making knowledge workers productive. Thirdly, they imply different consequences for the management of knowledge workers. This chapter closes abovementioned research gaps by summarising the indicators employed in five knowledge-intensive organisations from different business sectors for the assessment of knowledge workers’ performance, by subsequently deducing the specific challenges involved in the management of knowledge workers and by further delineating consequences for the management of knowledge workers – consequences affecting various knowledge-intensive industries.

What matters for knowledge work productivity?

2019 ◽  
Vol 41 (1) ◽  
pp. 209-227 ◽  
Author(s):  
Miikka Palvalin
Keyword(s):  
Conceptual Model ◽  
Knowledge Worker ◽  
Work Productivity ◽  
Knowledge Work ◽  
Well Being ◽  
Survey Method ◽  
Work Practices ◽  
Content Type ◽  
Study Results ◽  
What Matters

Purpose Knowledge work productivity is a well-studied topic in the existing literature, but it has focussed mainly on two things. First, there are many theoretical models lacking empirical research, and second, there is a very specific research regarding how something impacts productivity. The purpose of this paper is to collect empirical data and test the conceptual model of knowledge work productivity in practice. The paper also provides information on how different drivers of knowledge work productivity have an impact on productivity. Design/methodology/approach Through the survey method, data were collected from 998 knowledge workers from Finland. Then, confirmatory factor analysis was conducted to confirm the knowledge work productivity dimensions of the conceptual model. Later, regression analysis was used to analyse the impacts of knowledge factors on productivity. Findings This paper increases the understanding of what matters for knowledge work productivity, with statistical analysis. The conceptual model of knowledge work productivity consists of two major elements: the knowledge worker and the work environment. The study results showed that the knowledge worker has the biggest impact on productivity through his or her well-being and work practices. The social environment was also found to be a significant driver. The results could not confirm or refute the role of the physical or virtual environment in knowledge work productivity. Practical implications The practical value of the study lies in the analysis results. The information generated about the factors impacting productivity can be used to improve knowledge work productivity. In addition, the limited resources available for organisational development will have the greatest return if they are used to increase intangible assets, i.e., management and work practices. Originality/value While it is well known that many factors are essential for knowledge work productivity, relatively few studies have examined it from as many dimensions at the same time as this study. This study adds value to the literature by providing information on which factors have the greatest influence on productivity.

scholarly journals A Soft Systems Approach to Knowledge Worker Productivity: A Purposeful Activity Model for the Individual

2021 ◽  
Vol 11 (4) ◽  
pp. 110
Author(s):  
Helga Guðrún Óskarsdóttir ◽  
Guðmundur Valur Oddsson ◽  
Jón Þór Sturluson ◽  
Rögnvaldur Jóhann Sæmundsson
Keyword(s):  
Conceptual Framework ◽  
Systems Approach ◽  
Knowledge Worker ◽  
Knowledge Work ◽  
Lessons Learned ◽  
Extensive Literature ◽  
Worker Productivity ◽  
Systems Methodology ◽  
Soft Systems ◽  
The Individual

This research attempted to find and define holistic systems that affect the productivity of the knowledge worker (KW), using the soft systems methodology (SSM). It is not enough to look at the management and improvement of knowledge worker productivity (KWP) from the viewpoint of the organization. The viewpoint of the individual KW needs to be considered as well. The KW owns the means of production; they carry their knowledge in their heads and take it with them when changing jobs. This paper proposes a conceptual framework that describes the process in which the KW uses resources to execute actions to create tangible or intangible artifacts with the intention of generating value. It was based on interpretations and inferences made from an extensive literature review using the snowballing method. This paper highlights what implications the lessons learned from the conceptual framework have on managing and improving KWP and delves deeper into four key concepts: value in knowledge work, knowledge, personal resources, and competencies.

A valid and applicable measurement method for knowledge worker productivity

2018 ◽  
Vol 67 (9) ◽  
pp. 1764-1791 ◽  
Author(s):  
Jalil Heidary Dahooie ◽  
Mohammad Reza Ghezel Arsalan ◽  
Ali Zolghadr Shojai
Keyword(s):  
Knowledge Worker ◽  
Electric Power Industry ◽  
Knowledge Work ◽  
Data Envelopment ◽  
Productivity Measurement ◽  
Worker Productivity ◽  
Content Type ◽  
Fuzzy Data Envelopment Analysis ◽  
Proposed Model ◽  
Relative Productivity

Purpose The purpose of this paper is to propose a new method for knowledge worker productivity measurement which is based on valid principles and appropriate viewpoints. Design/methodology/approach Based on an extensive and thorough literature review the elements that need to be taken into consideration, while designing a method for knowledge worker productivity measurement, are determined and divided into principles and viewpoints. These elements must be incorporated into the design of knowledge worker productivity measurement methods so that the correctness and accuracy of these methods can be verified. The proposed model, which is based on appropriate principles and viewpoints, determines the outputs of knowledge work with respect to the tasks that a worker’s job includes. Considering nine measures, these outputs are evaluated using fuzzy numbers and, then, quantified. The inputs of knowledge work are knowledge, skills and abilities (KSAs) required to do the job. These inputs are identified and quantified using Job Element Method. Furthermore, fuzzy Data Envelopment Analysis is employed to model the productivity. Findings In this paper, the proposed method for knowledge worker productivity measurement follows both appropriate principles and viewpoints, simultaneously. In order to validate the obtained results and explore the applicability of the proposed method, a case study was carried out at an Iranian organization in electric power industry. Statistical analyses are employed to prove the validity of the results. Based on the obtained results, the productivity of a knowledge worker is said to be high when he/she delivers the expected amount of job outputs considering the values of his/her inputs (KSAs). Originality/value The originality of this paper is twofold. First, the extracted principles and viewpoints can serve as a guideline for the development of similar methods. Second, the proposed model offers an effective and efficient tool that can serve as the basis for the comparison among relative productivity of knowledge workers. Furthermore, the obtained results could form a basis to examine the productivity trend of each knowledge worker over different periods of time.

OMERACT Filter Evidence Supporting the Measurement of At-work Productivity Loss as an Outcome Measure in Rheumatology Research

2015 ◽  
Vol 43 (1) ◽  
pp. 214-222 ◽  
Author(s):  
Dorcas E. Beaton ◽  
Sarah Dyer ◽  
Annelies Boonen ◽  
Suzanne M.M. Verstappen ◽  
Reuben Escorpizo ◽  
...  
Keyword(s):  
Outcome Measurement ◽  
Contextual Factors ◽  
Productivity Loss ◽  
Work Productivity ◽  
Contextual Factor ◽  
Nominal Group ◽  
Future Research ◽  
Specific Work ◽  
Worker Productivity ◽  
Activity Impairment

Objective.Indicators of work role functioning (being at work, and being productive while at work) are important outcomes for persons with arthritis. As the worker productivity working group at OMERACT (Outcome Measures in Rheumatology), we sought to provide an evidence base for consensus on standardized instruments to measure worker productivity [both absenteeism and at-work productivity (presenteeism) as well as critical contextual factors].Methods.Literature reviews and primary studies were done and reported to the OMERACT 12 (2014) meeting to build the OMERACT Filter 2.0 evidence for worker productivity outcome measurement instruments. Contextual factor domains that could have an effect on scores on worker productivity instruments were identified by nominal group techniques, and strength of influence was further assessed by literature review.Results.At OMERACT 9 (2008), we identified 6 candidate measures of absenteeism, which received 94% endorsement at the plenary vote. At OMERACT 11 (2012) we received over the required minimum vote of 70% for endorsement of 2 at-work productivity loss measures. During OMERACT 12 (2014), out of 4 measures of at-work productivity loss, 3 (1 global; 2 multiitem) received support as having passed the OMERACT Filter with over 70% of the plenary vote. In addition, 3 contextual factor domains received a 95% vote to explore their validity as core contextual factors: nature of work, work accommodation, and workplace support.Conclusion.Our current recommendations for at-work productivity loss measures are: WALS (Workplace Activity Limitations Scale), WLQ PDmod (Work Limitations Questionnaire with modified physical demands scale), WAI (Work Ability Index), WPS (Arthritis-specific Work Productivity Survey), and WPAI (Work Productivity and Activity Impairment Questionnaire). Our future research focus will shift to confirming core contextual factors to consider in the measurement of worker productivity.

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