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Article
Publication date: 3 October 2016

Charlotte M. Karam and David A. Ralston

A large and growing number of researchers set out to cross-culturally examine empirical relationships. The purpose of this paper is to provide researchers, who are new to…

Abstract

Purpose

A large and growing number of researchers set out to cross-culturally examine empirical relationships. The purpose of this paper is to provide researchers, who are new to multicountry investigations, a discussion of the issues that one needs to address in order to be properly prepared to begin the cross-cultural analyses of relationships.

Design/methodology/approach

Thus, the authors consider two uniquely different but integrally connected challenges to getting ready to conduct the relevant analyses for just such multicountry studies. The first challenge is to collect the data. The second challenge is to prepare (clean) the collected data for analysis. Accordingly, the authors divide this paper into two parts to discuss the steps involved in both for multicountry studies.

Findings

The authors highlight the fact that in the process of collecting, there are a number of key issues that should be kept in mind including building trust with new team members, leading the team, and determining sufficient contribution of team members for authorship. Subsequently, the authors draw the reader’s attention to the equally important, but often-overlooked, data cleaning process and the steps that constitute it. This is important because failing to take serious the quality of the data can lead to violations of assumptions and mis-estimations of parameters and effects.

Originality/value

This paper provides a useful guide to assist researchers who are engaged in data collection and cleaning efforts with multiple country data sets. The review of the literature indicated how truly important a guideline of this nature is, given the expanding nature of cross-cultural investigations.

Details

Cross Cultural & Strategic Management, vol. 23 no. 4
Type: Research Article
ISSN: 2059-5794

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Article
Publication date: 11 January 2016

Mireille D. Hubers, Cindy L. Poortman, Kim Schildkamp, Jules M. Pieters and Adam Handelzalts

In this study, Nonaka and Takeuchi’s socialization, externalization, combination and internalization (SECI) model of knowledge creation is used to gain insight into the…

Abstract

Purpose

In this study, Nonaka and Takeuchi’s socialization, externalization, combination and internalization (SECI) model of knowledge creation is used to gain insight into the process of knowledge creation in data teams. These teams are composed of school leaders and teachers, who work together to improve the quality of education. They collaboratively create knowledge related to data use and to an educational problem they are studying. The paper aims to discuss these issues.

Design/methodology/approach

A qualitative micro-process case study was conducted for two data teams. The modes, transitions and content of the knowledge creation process were analyzed for all data team meetings over a two-year period. In addition, all team members were interviewed twice to triangulate the findings.

Findings

Results show that the knowledge creation process was cyclical across meetings, but more iterative within meetings. Furthermore, engagement in the socialization and internalization mode provided added value in this process. Finally, the SECI model clearly differentiated between team members’ processes. Team members who engaged more often in the socialization and internalization modes and displayed more personal engagement in those modes gained greater and deeper knowledge.

Research limitations/implications

The SECI model is valuable for understanding how teams gain new knowledge and why they differ in those gains.

Practical implications

Stimulation of active personal engagement in the socialization and internalization mode is needed.

Originality/value

This is one of the first attempts to concretely observe the process of knowledge creation. It provides essential insights into what educators do in professional development contexts, and how support can best be provided.

Details

Journal of Professional Capital and Community, vol. 1 no. 1
Type: Research Article
ISSN: 2056-9548

Keywords

Content available
Article
Publication date: 9 July 2020

Tina Peeters, Jaap Paauwe and Karina Van De Voorde

The purpose of this paper is to explore the key ingredients that people analytics teams require to contribute to organizational performance. As the information that is…

Abstract

Purpose

The purpose of this paper is to explore the key ingredients that people analytics teams require to contribute to organizational performance. As the information that is currently available is fragmented, it is difficult for organizations to understand what it takes to execute people analytics successfully.

Design/methodology/approach

To identify the key ingredients, a narrative literature review was conducted using both traditional people analytics and broader business intelligence literature. The findings were summarized in the People Analytics Effectiveness Wheel.

Findings

The People Analytics Effectiveness Wheel identifies four categories of ingredients that a people analytics team requires to be effective. These are enabling resources, products, stakeholder management and governance structure. Under each category, multiple sub-themes are discussed, such as data and infrastructure; senior management support; and knowledge, skills, abilities and other characteristics (KSAOs) (enablers).

Practical implications

Many organizations are still trying to set up their people analytics teams, and many others are struggling to improve decision-making by using people analytics. For these companies, this paper provides a comprehensive overview of the current literature and describes what it takes to contribute to organizational performance using people analytics.

Originality/value

This paper is designed to provide organizations and researchers with a comprehensive understanding of what it takes to execute people analytics successfully. By using the People Analytics Effectiveness Wheel as a guideline, scholars are now better equipped to research the processes that are required for the ingredients to be truly effective.

Details

Journal of Organizational Effectiveness: People and Performance, vol. 7 no. 2
Type: Research Article
ISSN: 2051-6614

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Article
Publication date: 25 September 2019

Torsten Maier, Joanna DeFranco and Christopher Mccomb

Often, it is assumed that teams are better at solving problems than individuals working independently. However, recent work in engineering, design and psychology…

Abstract

Purpose

Often, it is assumed that teams are better at solving problems than individuals working independently. However, recent work in engineering, design and psychology contradicts this assumption. This study aims to examine the behavior of teams engaged in data science competitions. Crowdsourced competitions have seen increased use for software development and data science, and platforms often encourage teamwork between participants.

Design/methodology/approach

We specifically examine the teams participating in data science competitions hosted by Kaggle. We analyze the data provided by Kaggle to compare the effect of team size and interaction frequency on team performance. We also contextualize these results through a semantic analysis.

Findings

This work demonstrates that groups of individuals working independently may outperform interacting teams on average, but that small, interacting teams are more likely to win competitions. The semantic analysis revealed differences in forum participation, verb usage and pronoun usage when comparing top- and bottom-performing teams.

Research limitations/implications

These results reveal a perplexing tension that must be explored further: true teams may experience better performance with higher cohesion, but nominal teams may perform even better on average with essentially no cohesion. Limitations of this research include not factoring in team member experience level and reliance on extant data.

Originality/value

These results are potentially of use to designers of crowdsourced data science competitions as well as managers and contributors to distributed software development projects.

Details

Team Performance Management: An International Journal, vol. 25 no. 7/8
Type: Research Article
ISSN: 1352-7592

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Book part
Publication date: 27 June 2015

Allan H. Church, Christopher T. Rotolo, Alyson Margulies, Matthew J. Del Giudice, Nicole M. Ginther, Rebecca Levine, Jennifer Novakoske and Michael D. Tuller

Organization development is focused on implementing a planned process of positive humanistic change in organizations through the use of social science theory, action…

Abstract

Organization development is focused on implementing a planned process of positive humanistic change in organizations through the use of social science theory, action research, and data-based feedback methods. The role of personality in that change process, however, has historically been ignored or relegated to a limited set of interventions. The purpose of this chapter is to provide a conceptual overview of the linkages between personality and OD, discuss the current state of personality in the field including key trends in talent management, and offer a new multi-level framework for conceptualizing applications of personality for different types of OD efforts. The chapter concludes with implications for research and practice.

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Abstract

Details

A Developmental and Negotiated Approach to School Self-Evaluation
Type: Book
ISBN: 978-1-78190-704-7

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Article
Publication date: 3 July 2017

Nora Gannon-Slater, Priya G. La Londe, Hope L. Crenshaw, Margaret E. Evans, Jennifer C. Greene and Thomas A. Schwandt

Data use cultures in schools determine data use practices. Such cultures can be muted by powerful macro accountability and organizational learning cultures. Further…

Abstract

Purpose

Data use cultures in schools determine data use practices. Such cultures can be muted by powerful macro accountability and organizational learning cultures. Further, strong equity-oriented data use cultures are challenging to establish. The purpose of this paper is to engage these cultural tensions.

Design/methodology/approach

The data discourse and decisions of four grade-level teams in two elementary schools in one district were studied through observation of 62 grade-level meetings over the course of a year. The observations focused on “data talk,” defined as the structure and content of team conversations about interim student performance data.

Findings

Distinct macro cultures of accountability and organizational learning existed in the two schools. The teams’ own data use cultures partly explained the absence of a focus on equity, and none of the teams used student performance data to make instructional decisions in support of the district’s equity aims. Leadership missed opportunities to cultivate an equity-focused data use culture.

Practical implications

School leaders who advocate that equity importantly guides data use routines, and can anticipate how cultures of accountability or organizational learning “show up” in data use conversations, will be better prepared to redirect teachers’ interpretations of data and clarify expectations of equity reform initiatives.

Originality/value

This study is novel in its concept of “data talk,” which provided a holistic but nuanced account of data use practices in grade-level meetings.

Details

Journal of Educational Administration, vol. 55 no. 4
Type: Research Article
ISSN: 0957-8234

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Book part
Publication date: 20 September 2018

Arwen H. DeCostanza, Katherine R. Gamble, Armando X. Estrada and Kara L. Orvis

Unobtrusive measurement methodologies are critical to implementing intelligent tutoring systems (ITS) for teams. Such methodologies allow for continuous measurement of team

Abstract

Unobtrusive measurement methodologies are critical to implementing intelligent tutoring systems (ITS) for teams. Such methodologies allow for continuous measurement of team states and processes while avoiding disruption of mission or training performance, and do not rely on post hoc feedback (including for the aggregation of data into measures or to develop insights from these real-time metrics). This chapter summarizes advances in unobtrusive measurement developed within Army research programs to illustrate the variety and potential that unobtrusive measurement approaches can provide for building ITS for teams. Challenges regarding the real-time aggregation of data and applications to current and future ITS for teams are also discussed.

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Article
Publication date: 21 February 2019

Carrie Dossick, Laura Osburn and Gina Neff

Through the study of visualizations, virtual worlds and information exchange, the purpose of this paper is to reveal the complex connections between technology and the…

Abstract

Purpose

Through the study of visualizations, virtual worlds and information exchange, the purpose of this paper is to reveal the complex connections between technology and the work of design and construction. The authors apply the sociotechnical view of technology and the ramifications this view has on successful use of technology in design and construction.

Design/methodology/approach

This is a discussion paper reviewing over a decade of research that connects three streams of research on architecture, engineering and construction (AEC) teams as these teams grappled with adapting work practices to new technologies and the opportunities these technologies promised.

Findings

From studies of design and construction practices with building information modeling and energy modeling, the authors show that given the constructed nature of models and the loose coupling of project teams, these team organizational practices need to mirror the modeling requirements. Second, looking at distributed teams, whose interaction is mediated by technology, the authors argue that virtual world visualizations enhance discovery, while distributed AEC teams also need more traditional forms of 2D abstraction, sketching and gestures to support integrated design dialogue. Finally, in information exchange research, the authors found that models and data have their own logic and structure and, as such, require creativity and ingenuity to exchange data across systems. Taken together, these streams of research suggest that process innovation is brought about by people developing new practices.

Originality/value

In this paper, the authors argue that technology alone does not change practice. People who modify practices with and through technology create process innovation.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 12 July 2013

Ali Elbireer, Julie Le Chasseur and Brooks Jackson

The Uganda Makerere University provides clinical laboratory support to over 70 clients in Uganda. With increased volume, manual data entry errors have steadily increased…

Abstract

Purpose

The Uganda Makerere University provides clinical laboratory support to over 70 clients in Uganda. With increased volume, manual data entry errors have steadily increased, prompting laboratory managers to employ the Six Sigma method to evaluate and reduce their problems. The purpose of this paper is to describe how laboratory data entry quality was improved by using Six Sigma.

Design/methodology/approach

The Six Sigma Quality Improvement (QI) project team followed a sequence of steps, starting with defining project goals, measuring data entry errors to assess current performance, analyzing data and determining data‐entry error root causes. Finally the team implemented changes and control measures to address the root causes and to maintain improvements. Establishing the Six Sigma project required considerable resources and maintaining the gains requires additional personnel time and dedicated resources.

Findings

After initiating the Six Sigma project, there was a 60.5 percent reduction in data entry errors from 423 errors a month (i.e. 4.34 Six Sigma) in the first month, down to an average 166 errors/month (i.e. 4.65 Six Sigma) over 12 months. The team estimated the average cost of identifying and fixing a data entry error to be $16.25 per error. Thus, reducing errors by an average of 257 errors per month over one year has saved the laboratory an estimated $50,115 a year.

Practical implications

The Six Sigma QI project provides a replicable framework for Ugandan laboratory staff and other resource‐limited organizations to promote quality environment. Laboratory staff can deliver excellent care at a lower cost, by applying QI principles.

Originality/value

This innovative QI method of reducing data entry errors in medical laboratories may improve the clinical workflow processes and make cost savings across the health care continuum.

Details

International Journal of Health Care Quality Assurance, vol. 26 no. 6
Type: Research Article
ISSN: 0952-6862

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