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Content and Text Analysis Methods for Organizational Research  

Rhonda K. Reger and Paula A. Kincaid

Content analysis is to words (and other unstructured data) as statistics is to numbers (also called structured data)—an umbrella term encompassing a range of analytic techniques. Content analyses range from purely qualitative analyses, often used in grounded theorizing and case-based research to reduce interview data into theoretically meaningful categories, to highly quantitative analyses that use concept dictionaries to convert words and phrases into numerical tables for further quantitative analysis. Common specialized types of qualitative content analysis include methods associated with grounded theorizing, narrative analysis, discourse analysis, rhetorical analysis, semiotic analysis, interpretative phenomenological analysis, and conversation analysis. Major quantitative content analyses include dictionary-based approaches, topic modeling, and natural language processing. Though specific steps for specific types of content analysis vary, a prototypical content analysis requires eight steps beginning with defining coding units and ending with assessing the trustworthiness, reliability, and validity of the overall coding. Furthermore, while most content analysis evaluates textual data, some studies also analyze visual data such as gestures, videos and pictures, and verbal data such as tone. Content analysis has several advantages over other data collection and analysis methods. Content analysis provides a flexible set of tools that are suitable for many research questions where quantitative data are unavailable. Many forms of content analysis provide a replicable methodology to access individual and collective structures and processes. Moreover, content analysis of documents and videos that organizational actors produce in the normal course of their work provides unobtrusive ways to study sociocognitive concepts and processes in context, and thus avoids some of the most serious concerns associated with other commonly used methods. Content analysis requires significant researcher judgment such that inadvertent biasing of results is a common concern. On balance, content analysis is a promising activity for the rigorous exploration of many important but difficult-to-study issues that are not easily studied via other methods. For these reasons, content analysis is burgeoning in business and management research as researchers seek to study complex and subtle phenomena.

Article

Entrepreneurial Teams  

Nicola Breugst

Entrepreneurial teams develop and exploit ideas in order to turn them into entrepreneurial ventures that they jointly own and manage. While these teams are crucial drivers for the success of their ventures, their work can be challenging because they operate under conditions of high autonomy, uncertainty, and interdependence. Thus, it is important to understand how entrepreneurial teams work together and jointly advance their ventures. Research has followed three overarching approaches to explore how entrepreneurial teams can succeed in their endeavors. First, one stream of research has aimed at connecting team inputs, such as team members’ experiences, to firm-level outcomes. In a second stream of research, scholars have focused on what happens within entrepreneurial teams in terms of team processes and emergent states. This approach has identified various mechanisms that translate inputs into outcomes. Third, an increasing number of studies have started to unravel the complexities that entrepreneurial teams experience in their work. Specifically, this research has considered the mutual influence of team members and has explored how teams work on their tasks and are shaped by this work. Despite these advancements, entrepreneurial team research faces numerous challenges arising from the complex interplay of team members and their ventures as well as from access to high-quality data. Because of these and other challenges, many research questions around entrepreneurial teams still need to be addressed to better understand their work. These emerging research efforts are likely to be facilitated by additional data sources, such as educational programs devoted to advancing entrepreneurial teams and modern technologies promising better access to rich data. Overall, entrepreneurial team research not only contributes to a more nuanced understanding of the entrepreneurial process but also provides support for these teams as they create and nurture their ventures.