Search results

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.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

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.

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.

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.