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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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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

The purpose of this paper is to explore how positive organizational behaviors, specifically team proactivity, can be captured through digital data and what determines content validity of these data. The aim is to enable scientifically rigorous HR analytics projects for measuring and managing organizational behavior.

Results are derived from interview data (N = 24) with team members, HR professionals and consultants of HR software.

Based on inductive qualitative content analysis, the authors clustered six data types generated/recorded by 13 different technological applications that were proposed to be informative of team proactivity. Four determinants of content validity were derived.

The overview of technological applications and resulting data types can stimulate diverse HR analytics projects, which can contribute to organizational performance. The authors suggest ways to control for the threats to content validity in the design of HR analytics or research projects.

HR analytics projects in the application field of managing organizational behavior are rare. This paper provides starting points for choosing data to measure team proactivity as one form of organizational behavior and guidelines for ensuring their validity.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to give an overview of the Jisc and Higher Education Statistics Agency (HESA) Library Data Labs project and its outputs. This collaboration involved bringing together cross-institutional library teams to produce proof of concept data-visualised dashboards using library analytics data that could be made available to others via the Heidi Plus service.

The teams used an agile approach, which adapted the agile methodology for non-technical and disparate team members. The key agile elements were followed, including the Scrum approach, whereby teams had a product owner, several development team members, a data wrangler and a scrum master. Many of the dashboards took inspiration from some of the earlier Jisc work on library analytics.

A wide variety of proof of concept dashboards were created addressing a range of library issues. These fell into two main categories for the cross-institutional teams, namely, comparing the Society of College, National and University Libraries (SCONUL) annual statistics results against the National Student Survey (NSS) data and collection management and analysis.

Some of the HESA data were potentially sensitive. In effect, this created a walled garden as some of the data were not designed for sharing. Furthermore, the data that the Jisc team used were restricted by publisher agreements, meaning that specific institutions’ usage could not be identified to others.

The paper provides insight into the Library Data Labs project and discusses a number of implications from the outcomes of the project. These are now being investigated by HESA, Jisc and individual institutions.

The purpose of this paper is to use an extreme case to identify and describe the nature of routines that might support processes and outcomes of data use, drawing from a framework developed by Coburn and Turner (2012a).

The author conducted a four-month case study (Stake, 1995) of an elementary school in a large urban school district that had implemented balanced score cards. The author identified a school that had strong qualities to support data use, including leadership and information systems.

Two school-level organizational routines facilitated teachers’ data use: collaborative teams and processes of inquiry. These routines stored knowledge about the types of data teachers ought to notice, and to a lesser extent, how they ought to interpret data and construct implications for practice. These routines also provided opportunities for single and double-loop learning (Argyris and Schön, 1996) and might contribute to improvements in student learning. This case provides an example of how a school negotiated external performance management pressures, and maintained their professional autonomy, focussing on internally initiated assessments.

Relatively little research has described what organizational routines support data use among practitioners. In addition to describing two routines, this case also demonstrated the need to frame these routines as organizational routines for learning. To further develop these routines, the author drew on the notion of the knowledge-creating company (Nonaka and Takeuchi, 1995) to explain how the school used their organizational routines to share tacit knowledge (socialization), and to convert tacit knowledge to explicit knowledge (externalization), which supported instructional innovations.

Existing conceptual, empirical and case studies evidence suggests that manufacturing industries find the joint implementation of Kaizen philosophy initiatives. However, the existing practices rarely demonstrated in a single framework and implementation procedure in a structure nature. This paper, therefore, aims to develop, validate and practically test a framework and implementation procedure for the implementation of integrated Kaizen in manufacturing industries to attain long-term improvement of operational, innovation, business (financial and marketing) processes, performance and competitiveness.

The study primarily described the problem, extensively reviewed the current state-of-the-art literature and then identified a gap. Based on it, generic and comprehensive integrated framework and implementation procedure is developed. Besides, the study used managers, consultants and academics from various fields to validate a framework and implementation procedure for addressing business concerns. In this case, the primary data was collected through self-administered questionnaire, and 244 valid questionnaires were received and were analyzed. Furthermore, the research verified the practicability of the framework by empirically exploring the current scenario of selected manufacturing companies.

The research discovered innovative framework and six-phase implementation procedure to fill the existing conceptual gap. Furthermore, the survey-based and exploratory empirical analysis of the research demonstrated that the practice of the proposed framework based on structured procedure is valued and companies attain the middling improvements of productivity, delivery time, quality, 5S practice, waste and accident rate by 61.03, 44, 52.53, 95.19, 80.12, and 70.55% respectively. Additionally, the companies saved a total of 14933446 ETH Birr and 5,658 M² free spaces. Even though, the practices and improvements vary from company to company, and even companies unable to practice some of the unique techniques of the identified CI initiatives considered in the proposed framework.

All data collected in the survey came from professionals working for Ethiopian manufacturing companies, universities and government. It is important to highlight that n = 244 is high sample size, which is adequate for a preliminary survey but reinforcing still needs further survey in terms of generalization of the results since there are hundreds of manufacturing companies, consultants and academicians implementing and consulting Kaizen. Therefore, a further study on a wider Ethiopian manufacturing companies, consultants and academic scale would be informative.

This work is very important for Kaizen professionals in the manufacturing industry, academic and government but in particular for senior management and leadership teams. Aside from the main findings on framework development, there is some strong evidence that practice of Kaizen resulted in achieving quantitative (monetary and non-monetary) and qualitative results. Thus, senior management teams should use this research out to practice and analyze the effect of Kaizen on their own organizations. Within the academic community, this study is one of the first focusing on development, validating and practically testing and should aid further study, research and understanding of Kaizen in manufacturing industries.

So far, it is rare to find preceding studies proposed, validated and practically test an integrated Kaizen framework with the context of manufacturing industries. Thus, authors understand that this is the very first research focused on the development of the framework for manufacturing industries continuously to be competitive and could help managers, institutions, practitioners and academicians in Kaizen practice.