CAT Of The Month: Knowledge Worker Productivity

 

CAT

 A Critically Appraised Topic (CAT) is a concise summary (2 – 3 pages max) of the research evidence on a practical question / problem with short, bottom-line recommendations. This month’s CAT is about knowledge worker productivity.

 

CEBMa supports organizations by conducting CATs or Rapid Evidence Assessments (REA). For further information, please contact us.

 

 


 

Critical appraisal of the research literature on the factors associated with knowledge worker productivity 

 

Rationale for this review

In the summer of 2013, members of the Workplace Performance Innovation Network (PIN), an initiative of AWA, identified an interest in looking into the subject of professional productivity in knowledge workers. This is to support a strategic initiative that seeks to understand and leverage those factors that have the greatest impact on knowledge worker productivity. The PIN members wish to understand what academic research has discovered about the determinants of productivity, and how these can be measured. AWA approached the Center for Evidence-Based Management (CEBMa) to undertake a review of the best available evidence about the most widely studied determinants of productivity and what is known of their effect. This review will present an overview of this evidence. 

 

Main review question: What will the review answer?

“Which of the factors that have an impact on the productivity of knowledge workers are most widely studied and what is known of their effect?”

 

Search Strategy: How was the research evidence sought?

The following four databases were used to identify studies: ABI/INFORM Global from ProQuest, Business Source Premier from EBSCO and PsycINFO from Ovid. The following generic search filters were applied to all databases during the search: 

  1. Scholarly journals, peer-reviewed
  2. Published in the period 1980 to 2013
  3. Articles in English

A search was conducted using combinations of different search terms, such as ‘productivity’, ‘performance’, ‘knowledge work’ and ‘knowledge based business’. We conducted 5 different search queries, which yielded a total of 570 studies. All queries, criteria and search results are described in detail in Annex I.

 

Selection: How were the included studies selected?

The following two inclusion criteria were applied to the selection of studies:

  1. Type of study: only quantitative studies were included
  2. Outcome measurement: the only studies included were those in which the effect of an independent variable on the productivity, performance or innovation of individual employees, teams or organizations was measured.

Study selection took place in two phases. First, the titles and abstracts of the 570 studies were screened for their relevance to this review. In case of doubt, lack of information, or disagreement, the study was included. Duplicate publications were removed. This first phase yielded 52 meta-analyses and 109 single studies. Second, studies were selected for inclusion based on the full text of the article. This second phase yielded 24 single studies and 35 meta-analyses. A meta-analysis is a study that uses statistical techniques to combine the results of a number of studies published on the same topic to obtain a pooled quantitative estimate of the overall effect of a particular variable on a specific outcome. An overview of the selection procedure is provided in Annex II. 

 

Critical appraisal and classification: How was the quality of the evidence found judged?

From each study we extracted and interpreted information relevant for the review question, such as sample size, population, research design, independent variable, outcome measures, effect size and findings. The research design of the included studies was systematically assessed and categorized according to Campbell’s and Petticrew’s classification system (Petticrew & Roberts, 2006; Shadish, et al., 2002). The following five levels of evidence were used in the classification:

Level A: randomized controlled studies
Level B: non-randomized controlled studies with a pretest
Level C: controlled studies without a pretest or uncontrolled studies with a pretest Level D: uncontrolled studies without pretest, qualitative studies
Level X: expert opinion, non-systematical reviews of the literature

 

Limitations of the evidence-base

Most of the studies reviewed in the 35 meta-analyses employed cross-sectional designs (level D/C) and controlled or quasi-experimental studies (level C/B). Some of the meta- lacked sufficient information about the study data. The overall internal validity of the meta-analyses was therefore moderate. Of the 24 single studies only 2 studies qualified as a Level A or B study, the remaining 22 had a low level of evidence quality, in other words research with a low internal validity. In addition, most meta-analyses provided insufficient information on the design of the studies included. Finally, because concessions were made in relation to the breadth and depth of the search process, such as the exclusion of unpublished research, this review is prone to selection bias in that it is not a completely comprehensive review of all the published and all the unpublished evidence on the topic.

 

Results: What was found?

1. A total of 76 factors were identified, accounting for more than 145 effect sizes. An overview of all factors and effect sizes is provided in Annex III (meta-analyses) and Annex IV (single studies).

2. Based on the analysis of the 59 included studies We can assume that, with regard to the productivity or performance of knowledge workers, the following 5 factors are most widely studied:

 

Variable

Productivity measure

Nr of studies

  1. task cohesion

team performance

100+

  1. relationship conflicts

employee performance and team performance & innovation

70+

  1. transactive memory

team performance

60+

  1. social cohesion

team performance

40+

  1. support for innovation

team innovation

30+

 

3. Based on the analysis of the 35 meta-analyses we can conclude that, with regard to the productivity or performance of knowledge workers, the following 3 factors tend to have the highest association.

 

Variable

Productivity measure

Mean correlation weighted   by sample size

  1. social cohesion

team performance, hard outcome

.49

team performance, behavioral

.70

  1. perceived supervisory support

employee performance

.53

  1. information sharing

team performance

.51

 

Comments

The question of this review is about factors that have an impact on knowledge worker productivity. As stated above, most of the included studies have a moderate internal validity. Internal validity is an indicator of the extent that a cause-and-effect relationship between an intervention and its outcome is well founded. Or, put differently, a weak internal validity signifies that alternative explanations for the outcome found are very possible. This means that there is no certainty that factors that demonstrate a high association with the productivity or performance of knowledge workers can also be used as a lever to increase knowledge worker productivity. 

 


 

Social Cohesion

Social Cohesion has been demonstrated to have a positive relationship with team performance. The strength of this relationship depends on organisational setting, for example, it seems to play an important role at the start of a project, and again when the ongoing life of the team becomes established and the need for creativity has diminished. Also, Social Cohesion is stronger, and hence important, in smaller rather than larger teams. Finally, the strength of the relationship between cohesion and performance depends on the type of team, for example: it strongly predicts performance in teams with uncertain and complex tasks (e.g. project or R&D teams), and has a higher association with performance in knowledge work teams rather than in production work teams. Social Cohesion may facilitate a knowledge work team’s greater need for communication and knowledge. 

 

The level of social cohesion can be measured with the following five questions adapted from the Group Cohesion Questionnaire (GCQ, Carless & De Paola 2000) and O’Reilly’s questionnaire (1985). These questionnaires were developed for co-located teams, and therefore the wording may be adapted for assessing virtual team cohesion.

1. Members of our team like to spend time together outside of work hours

2. Members of our team get along with each other

3. Members of our team would rather get together as a team than go out on their own

4. Members of our team defend each other from criticism by outsiders

5. Members of our team help each other on the job 

Strongly agree / Somewhat agree / Neither agree or disagree/ Somewhat disagree / Strongly disagree

 

Perceived supervisory support

When employees receive feedback from and interact with their supervisor, they form perceptions of how the supervisor supports them. This may manifest itself based on how employees feel the supervisor helps them in times of need, praises them for a job well done or recognizes them for extra effort. This is known as perceived supervisory support (PSS), which is defined as employees’ global beliefs concerning the extent to which the supervisor values their contribution and cares about their well-being. Several studies show that perceptions employees have of the supervisors’ support for them impacts organizational objectives such as performance, organizational commitment, job satisfaction and turnover intentions.

 

The level of perceived supervisory and organizational support can be measured with the following six questions adapted from the validated Survey of Perceived Organizational Support (SPOS) by Eisenberger et al (1986).

 

Perceived supervisory support

1. My supervisor is willing to extend him- or herself in order to help me perform my job the best of my ability

2. My supervisor takes pride in my accomplishments at work

3. My supervisor tries to make my job as interesting as possible

 

Perceived organizational support

1. The organization values my contribution to its well-being

2. The organization strongly considers my goals and values

3. The organization really cares about my well-being 

Strongly agree / Somewhat agree / Neither agree or disagree/ Somewhat disagree / Strongly disagree

 

Information sharing

Information sharing (IS) refers to the extent to which teams are utilizing the individual members’ distinctive knowledge for the team’s benefit. Various researchers consider IS as a major source of innovation and performance improvement. IS is particularly critical in organizations that rely on knowledge-worker teams to deliver products and services. Especially if complex problems have to be addressed, IS is indispensable in that it allows team members to share their knowledge and past experiences and exchange and discuss ideas, which is particularly important for the generation of new ideas (Hulsheger et al, 2009). In the past two decades different conceptualizations and definitions of IS have been developed. For example, Mesmer-Magnus (2009) defines IS as “a central process through which team members collectively utilize their available informational resources” and Bunderson and Sutcliffe (2002) define it as “conscious and deliberate attempts on the part of team members to exchange work-related information, keep one another apprised of activities, and inform one another of key developments.” 

 

Transactive Memory System

The idea of TMS was developed by researching how couples with close relationships thought and sometimes took action together. The researcher noticed that the couples developed a shared memory through collaboration, but without conscious effort. The result was they effectively and efficiently shared each other’s memory capabilities (in terms of storage and retrieval) and capacity (in terms of volume and subject preferences). TMS have also been found to function in some close knit teams. A team TMS is a kind of collective mind based on communication about knowledge, expertise and team member experience. It enhances team performance since team members know:

– How best to remind one another of what they know.

– How best to prompt quick recall of information from each other.

– How best to ask for help using TMS knowledge and expertise.

 

A TMS can be thought of as a way for teams to collaboratively share the effort of learning, remembering and communicating. A TMS gives members quick coordinated access to one another’s expertise. For knowledge worker teams this is particularly advantageous, since it speeds access to a greater amount of expertise.  

 

The level of IS and TMS can be measured with the following five questions adapted from questionnaires by Bock et al (2005), Yeung Choi (2010), Lewis (2003) and Bunderson and Sutcliffe (2002)

 

Information sharing:

1. Our team members share their work reports and official documents with other team members.

2. Our team members share their experience or know-how with other team members.

3. Information to make key decisions is freely shared among the members of the team

 

Transactive memory system:

1. Our team members trust that other members’ knowledge is credible.

2. Our team members are confident of relying on the information that other team members bring to the discussion.