Meeting Measures: Feedback from Zoom

I created a website to give feedback to people on their virtual meetings. This website (http://www.meetingmeasures.com) relies on the code I’ve shared in past posts on how to quantify virtual meetings. The purpose of the site is to (a) unobtrusively capture people’s behavior in virtual meetings, (b) give people feedback on their presence and contributions in virtual meetings, and (c) suggest ways to improve their leadership and/or engagement in virtual meetings. There are currently options to incorporate survey data into the dashboard, as well.

This was a fun project to build. So far, I’ve administered > 100 meetings through the website. If you are interested in partnerships that involve the potential for research on virtual meeting behavior, please reach out.

Start-Up Teams: A Multidimensional Conceptualization, Integrative Review of Past Research, and Future Research Agenda

Knight, A. P., Greer, L. L., & de Jong, B. (2020). Start-up teams: A multidimensional conceptualization, integrative review of past research, and future research agenda. Academy of Management Annals, 14, 231-266.

Abstract. Academic interest in start-up teams has grown dramatically over the past 40 years, with researchers from a wide variety of disciplines actively studying the topic. Although this widespread interest is encouraging, a review of the literature reveals a lack of consensus in how researchers conceptualize and operationally define start-up teams. A lack of consensus on the core phenomenon—a foundational part of a strong paradigm—has stifled the systematic advancement of knowledge about start-up teams, which has downstream implications for the viability of this field of research. To advance the development of a stronger paradigm, we present a multidimensional conceptualization of start-up teams that is derived from points of consensus in existing definitions. Our multidimensional conceptualization accounts for the fact that, although all are under the umbrella of the concept of “start-up team,” start-up teams vary in a set of key ingredients—ownership of equity, autonomy of strategic decision-making, and entitativity. This conceptualization serves as a framework for reviewing and beginning to integrate past research on start-up teams. It also serves as a framework for guiding and informing an integrated program of future research on start-up teams. By introducing a multidimensional conceptualization of start-up teams, we highlight the value of considering the defining ingredients of start-up teams for furthering a stronger paradigm.

On the relation between felt trust and actual trust: Examining pathways to and implications of leader trust meta-accuracy

Campagna, R. L., Dirks, K. T., Knight, A. P., Crossley, C., & Robinson, S. L. (In Press). On the relation between felt trust and actual trust: Examining pathways to and implications of leader trust meta-accuracy. Journal of Applied Psychology.

Abstract. Research has long emphasized that being trusted is a central concern for leaders (Dirks & Ferrin, 2002), but an interesting and important question left unexplored is whether leaders feel trusted by each employee, and whether their felt trust is accurate. Across two field studies, we examined the factors that shape the accuracy of leaders’ felt trust—or, their trust meta-accuracy—and the implications of trust meta- accuracy for the degree of relationship conflict between leaders and their employees. By integrating research on trust and interpersonal perception, we developed and tested hypotheses based on two theoretical mechanisms—an external signaling mechanism and an internal presumed reciprocity mechanism—that theory suggests shape leaders’ trust meta-accuracy. In contrast to the existing literature on felt trust, our results reveal that leader trust meta-accuracy is shaped by an internal mechanism and the presumed reciprocity of trust relationships. We further find that whether trust meta-accuracy is associated with positive relational outcomes for leaders depends upon the level of an employee’s actual trust in the leader. Our research contributes to burgeoning interest in felt trust by elucidating the mechanisms underlying trust meta-accuracy and suggesting practical directions for leaders who seek to accurately understand how much their employees trust them.

On the emergence of collective psychological ownership in new creative teams

Gray, S. M., Knight, A. P., & Baer, M. (2020). On the emergence of collective psychological ownership in new creative teams. Organization Science, 31, 141-164.

Abstract. We develop and test a theoretical model that explains how collective psychological ownership—shared feelings of joint possession over something—emerges within new creative teams that were launched to advance one person’s (i.e., a creative lead’s) preconceived idea. Our model proposes that such teams face a unique challenge—an initial asymmetry in feelings of psychological ownership for the idea between the creative lead who conceived the idea and new team members who are beginning to work on the idea. We suggest that the creative lead can resolve this asymmetry and foster the emergence of collective psychological ownership by enacting two interpersonal behaviors—help seeking and territorial marking. These behaviors build collective ownership by facilitating the unifying, centripetal force of team identification and preventing the divisive, centrifugal force of team ownership conflict. Our model also proposes that collective ownership positively relates to the early success of new creative teams. The results of a quantitative study of 79 creative teams participating in an entrepreneurship competition provided general support for our predictions, but also suggested refinements as to how a creative lead’s behavior influences team dynamics. The findings of a subsequent qualitative investigation of 27 teams participating in a university startup launch course shed additional light on how collective ownership emerges in new creative teams launched to advance one person’s idea.

Dyadic data analysis

Knight, A. P., & Humphrey, S. E. (2019). Dyadic data analysis. In S. E. Humphrey and J. M. LeBreton (Eds.), The Handbook for Multilevel Theory, Measurement, and Analysis, pp. 423-447. Washington, DC: American Psychological Association.

Accompanying R functions for the social relations model: http://apknight.org/pdSRM.R

Abstract. Many foundational theories in the social sciences rely upon assumptions about dyadic interpersonal perceptions, behaviors, and relationships. This chapter provides a broad introduction to foundational concepts and techniques in analyzing dyadic data. The authors describe in detail one specific approach to dyadic data analysis—the social relations model—and provide software functions for conducting the analysis using multilevel modeling in R. The value of dyadic data analysis is illustrated through a discussion of prior publications that have used this approach. The authors also provide a step-by-step empirical example of how to use the social relations model with multilevel modeling in R, focused on dyadic trust in workgroups. The chapter concludes with a discussion of alternative approaches, beyond the social relations model, for analyzing dyadic data.

Innovations in unobtrusive methods

Knight, A. P. (2018). Innovations in unobtrusive methods. In A. Bryman and D. A. Buchanan (Eds.), Unconventional Methodology in Organization and Management Research, pp. 64-83. Oxford: Oxford University Press.

Abstract. Twenty years ago, engineer and computer scientist Rosalind Picard (1997, p.228) imagined a future in which ‘a financial analyst might combine his cell phone, pager, online stock reports, analysis software, and personal email agent into one computer that fits in a belt, watch and shirt pocket’.  Clearly the future is now.  An estimated 1.4 billion people owned a smartphone in 2013 – more than one fifth of the global population (Heggestuen, 2013).  By 2020, that proportion is expected to rise to approximately 70 percent (Ericsson, 2015).  And smartphones are just the tip of the iceberg, as a proliferation of internet-connected devices expands the linkages among humans, computers, and networks.  Consider just a few of the devices released recently.  Glasses developed by companies like Google and Snap enable users to capture and share multimedia content in real-time; wristbands like those developed by Fitbit, Apple, and Samsung facilitate fitness tracking, payments, and more.

The ubiquity of connected devices (Swan, 2012) – and the metrics that they unobtrusively capture – has led data to become increasingly central to the global economy.  Companies have integrated novel unobtrusive data streams into their business models and operations (e.g. Walker, 2012; Wilson, 2013).  These data streams can elucidate consumer preferences and responses to advertising, enhance human resource practices, and improve collaboration networks – to name just a few publicized applications.

Much like new data streams have enriched contemporary businesses, innovative unobtrusive methods hold great promise for researchers who study organizational functioning (Tonidandel et al., 2016).  The idea that researchers can benefit from using unobtrusive methods is certainly not new.  More than half a century ago, Webb and colleagues (1966) implored researchers in their classic book Unobtrusive Measures to use a more diverse set of data streams in their work, noting that, ‘Today the dominant mass of social science research is based upon interviews and questionnaires.  We lament this overdependence upon a single fallible method’ (pp.1-2).  Notwithstanding a steady drumbeat of pleas over the years for researchers to use unobtrusive methods (e.g. Hill et al., 2014; Webb and Weick, 1979), survey methods continue to dominate the literature, especially in organizational behaviour, and researchers still often rely on a single data source (Podsakoff et al., 2012; Scandura and Williams, 2000).

The purpose of this chapter is to describe a new suite of unobtrusive methods, such as the traces that people leave throughout the digital world as they search the Internet, post content on social media, and navigate an increasingly digitally-connected physical world.  These methods, which did not exist when Webb and colleagues published their book, make it easier and cheaper for researchers to use unobtrusive methods than ever before.  As a result, we social science researchers have fewer and fewer excuses for relying on a single source of data, obtrusively acquired, in empirical studies.

Organizational affective tone

Knight, A. P., Menges, J. I., & Bruch. H. (2018). Organizational affective tone: A meso perspective on the origins and effects of consistent affect in organizations. Academy of Management Journal, 61, 191-219.

Abstract. Grounded in an open systems perspective, we build and test new theory about how the kinds of industries in which an organization participates influences organizational affective tone and connects to workforce strain. We propose that the more an organization’s activities lie in consumer-centric industries (e.g., service, retail), the more positive and less negative the organization’s affective tone. We connect consumer-centric industry participation and affective tone by explaining how personnel policies and organizational structure generate and sustain consistent positive and negative affect throughout an organization. Additionally, we examine the effects of organizational affective tone on workforce strain. The results of a survey-based study of 24,015 human resource managers, top management team members, and employees of 161 firms largely support our predictions. We discuss the implications of considering macro contextual factors for understanding affect in organizations.

Resources and relationships in entrepreneurship

Huang, L., & Knight, A. P. (2017). Resources and relationships in entrepreneurship: An exchange theory of the development and effects of the entrepreneur-investor relationship. Academy of Management Review, 42, 80-102.

Abstract. We develop a theoretical model, grounded in exchange theory, about the process through which relationships between entrepreneurs and investors develop and influence the growth of new ventures. Our theory highlights the multifaceted relationships that entrepreneurs and investors share—comprising both affective and instrumental dimensions—and the bidirectional exchanges of social and financial resources that build these relationships over time. An exchange theory perspective sheds light on the emergence of different patterns of relationship development over time and how different kinds of resource exchange contribute to new venture growth, contingent on the core problems that a venture faces at a given stage of development. We discuss implications of an exchange perspective on resources and relationships in entrepreneurship for theory, research, and practice.

Using recurrence analysis to examine group dynamics

Knight, A. P., Kennedy, D. M., McComb, S. A. (2016). Using recurrence analysis to examine group dynamics. Group Dynamics: Theory, Research, and Practice, 20, 223-241.

Abstract. This article provides an accessible introduction to recurrence analysis—an analytical approach that has great promise for helping researchers understand group dynamics. Recurrence analysis is a technique with roots in the systems dynamics literature that was developed to reveal the properties of complex, nonlinear systems. By tracking when a system visits similar states at multiple points in its life—and the form or pattern of these recurrences over time—recurrence analysis equips researchers with a set of new metrics for assessing the properties of group dynamics, such as recurrence rate (i.e., stability), determinism (i.e., predictability), and entropy (i.e., complexity). Recent work has shown the potential value of recurrence analysis across a number of different disciplines. To extend its use within the domain of group dynamics, the authors present a conceptual overview of the technique and give a step-by-step tutorial on how to use recurrence analysis to study groups. An exemplar application of recurrence analysis using dialogue-based data from 63 three-person student groups illustrates the use of recurrence analysis in examining how groups change their focus on different processes over time. This is followed by a discussion of variations of recurrence analysis and implications for research questions within the literature on groups. When group researchers track group processes or emergent states over time, and thus compile a time series dataset, recurrence analysis can be a useful technique for measuring the properties of groups as dynamic systems.

The impact of environment and occupation on the health and safety of active duty air force members

Erich, R., Eaton, M., Mayes, R., Pierce, L., Knight, A. P., Genovesi, P., Escobar, J., Mychalczuk, G., Selent, M. (2016). The impact of environment and occupation on the health and safety of active duty Air Force members: Database development and de-identification. Military Medicine, 181, 821-826.

Abstract. Preparing data for medical research can be challenging, detail oriented, and time consuming. Transcription errors, missing or nonsensical data, and records not applicable to the study population may hamper progress and, if unaddressed, can lead to erroneous conclusions. In addition, study data may be housed in multiple disparate databases and complex formats. Merging methods may be incomplete to obtain temporally synchronized data elements. We created a comprehensive database to explore the general hypothesis that environmental and occupational factors influence health outcomes and risk-taking behavior among active duty Air Force personnel. Several databases containing demographics, medical records, health survey responses, and safety incident reports were cleaned, validated, and linked to form a comprehensive, relational database. The final step involved removing and transforming personally identifiable information to form a Health Insurance Portability and Accountability Act compliant limited database. Initial data consisted of over 62.8 million records containing 221 variables. When completed, approximately 23.9 million clean and valid records with 214 variables remained. With a clean, robust database, future analysis aims to identify high-risk career fields for targeted interventions or uncover potential protective factors in low-risk career fields.