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Developments in the social neurosciences over the past two decades have rendered problematic the main knowledge elicitation techniques currently in use by strategy researchers, as a basis for revealing actors’ mental representations of strategic knowledge. Extant elicitation techniques were advanced during an era when cognitive scientists and organizational researchers alike were preoccupied with the basic information of processing limitations of decision makers and means of addressing them, predicated on an outmoded conception of strategists as affect-free, cognitive misers. The need to adapt these techniques to enable the investigation of the emotional content and structure of actors’ mental representations is now a pressing priority for the advancement of theory, research, and practice pertaining to several interrelated areas of strategic management, from dynamic capabilities development, to upper echelons theory, to strategic consensus formation. Accordingly, in this chapter, we report the findings of two studies that investigated the feasibility of adapting the repertory grid, a robust method, widely known and well used in strategic management, for this purpose. Study 1 elicited a series of commonly mentioned strategic issues (the elements) from a sample of senior managers similar in composition to the sample recruited to the second study. Study 2 participants evaluated the elements elicited in Study 1 in relation to a series of researcher-supplied bipolar attributes (the constructs), based on the well-known affective circumplex model of human emotions. In line with expectations, a series of vector-based multivariate analyses revealed a number of interesting similarities and variations among participants in terms of the basic structure and emotional salience of the issues under consideration.

I draw on psychological and sociological theories of affect to depict the relatively stable set of moods and emotions that an individual experiences in social interactions with a given person (relational affect) as a fundamental engine of social action influencing both how and with whom employees perform assigned tasks. I discuss an approach to define and measure relational affect that complements the typical network approach to affect. I then explore motivational mechanisms through which relational affect influences task tie formation and functioning. I conclude that relational affect contributes directly to individuals’ ability to achieve task goals, and to organizational functioning generally.

Proactivity is a type of goal-directed work behavior in which individuals actively take charge of situations to bring about future change in themselves or their organization. In this chapter, we draw on goal-regulation research to review conceptual and empirical evidence that elucidates some of the complex links of affective experience and employee proactivity. We identify the different ways in which affective experience influences different stages of proactivity, including employees’ efforts in setting a proactive goal (envisioning), preparing to implement their proactive goal (planning), implementing their proactive goal (enacting), and engaging in learning from their proactive goal process (reflecting). Overall, our review suggests an important, positive role of high-activated positive trait affectivity and moods in motivating proactivity across multiple goal stages, as compared to low-activated positive affectivity and moods. The role of negative affect is mixed, and likely depends on both its valence and the stage of proactivity that is being considered. We identify a lack of research on the role of discrete emotions for employee proactivity. We discuss future avenues for research, particularly the roles of intra- and inter-personal emotion regulation for proactivity and of affective embeddedness of proactive processes in the social environment of organizations.

Established measures of work values were often developed without consideration of age‐related differences, and thus might not be sensitive for values that are only relevant for specific subgroups (i.e. older workers). Therefore, the purpose of this paper is to introduce a new measure that reflects a broad range of different work values including those of special interest for older workers (generativity values). The Munster Work Value Measure (MWVM) covers 21 work values from five value clusters, and combines a rating and ranking version.

The theoretical concept of the MWVM integrates various approaches from work motivation research. Reliability and validity of the MWVM are examined in two studies (n=81, n=471) using confirmatory factor analysis and multidimensional unfolding as well as concurrent data of organizational citizenship behavior and age‐related differences in work values.

The assumed structure of the MWVM was largely supported in both studies and for both the ranking and the rating versions of the MWVM. Moreover, correlational data supported the external validity of the MWVM.

Further validation research is desirable, together with benchmark data for specific subgroups (age, gender, occupations).

The MWVM qualifies as an efficient screening tool of motivational profiles and provides a basis for age‐sensitive human resource management.

The MWVM is the first measure that covers a broad range of work values including those of particular importance to older workers. The MWVM is available at the authors' web site.

The purpose of this paper is to test whether work engagement can be predicted by two core dimensions, energy and involvement, both at the individual and team levels.

Based on the circumplex model of affective well-being (Russell, 1980), the authors propose the work engagement grid and collect data on individual and team work engagement (TWE) from two different samples (n=1,192 individuals).

Results show a significant positive relationship between the individual engagement grid and individual work engagement. However, only the energy dimension significantly predicted TWE. The authors also provide evidences for the relationship between the engagement grid and related variables (e.g. adaptive performance, team cohesion, satisfaction), and show that the combination of energy and involvement present smaller correlations with those variables than the complete engagement scales.

Data were collected from simulation samples, therefore generalization of the findings must be done with caution. The findings allow for developing a brief measure of work engagement, particularly useful for longitudinal or diary study designs.

When teams are the work unit, the displays of energetic behaviors ought to be fostered in order to boost collective engagement.

The authors add to the existing literature on work engagement, concluding that individual and team-level work engagement have structural differences between them, with the collective construct being dependent on external manifestations of energy, and that individual work engagement needs a cognitive component of absorption in order to foster performance.

The aim of this study is to describe work-engaged teams in terms of interpersonal interaction.

Six teams (N = 31 individuals) were videotaped during a decision-making task, for one hour. Based on a priori defined categories, the authors coded the videos in terms of the degree of interaction between team members, the physical distance between members, the degree of team’s activation and the valence of their interaction. The videos were also coded in terms of motivational and affective processes. Team work engagement was assessed using questionnaires.

Highly engaged team members work physically close and have an increment on their interactions up until the task’s temporal midpoint. They have an initial peak of activation and show more positive emotional valence in the first and the last moments of the task. The most interpersonal processes used are affective. The worst performing team had the highest initial interaction levels followed by an abrupt decrease both in their levels of interaction and in their levels of activation. Simultaneously, they present higher peaks of positive emotional valence.

Although engaged teams are essentially characterized by the presence of positive interactions, it is fundamental to alternate more “exited” and fun moments with more task focused ones and collective interaction moments with individual work.

This study answers to Kozlowski and Chao’s (2012) call for studying emergence in a more direct way, using qualitative analysis of video data.

Experience sampling methods (ESM) have enabled researchers to capture intensive longitudinal data and how worker well-being changes over time. The conceptual advances in understanding the variability of well-being are discussed. These emerging forms in the literature include affective inertia, affective variability, affective reactivity, and density distributions. While most ESM research has relied on the active provision of data by participants (i.e., self-reports), technological advances have enabled different forms of passive sensing that are useful for assessing and tracking well-being and its contextual factors. These include accelerometer data, location data, and physiological data. The strengths and weaknesses of passively sensed data and future ways forward are discussed, where the use of both active and passive forms of ESM data in the assessment and promotion of worker well-being is expected.

Scholars have argued that different forms of proactive behaviors (e.g., career initiative, feedback seeking, and taking charge) all involve employees’ self-initiated and future-focused efforts to bring about change in a situation (Parker et al., 2006). There are at least three important elements that define proactivity: future-focus, change-orientation, and self-initiation (Frese & Fay, 2001; Parker et al., 2006). First, proactive behavior is future-focused, which means that this action is targeted at anticipated problems or at opportunities with a long-term focus. Second, proactive behavior is change-oriented, involving not just reacting to a situation but being prepared to change that situation in order to bring about a different future. Third, and underpinning the prior two elements, proactive behavior is self-initiated, which means that employees initiate a proactive goal without being told to, or without requiring explicit instructions from supervisors. Accordingly, proactivity has also been conceived of as a process in which employees generate and implement, under their own direction, a proactive goal to bring about a different future (Bindl, Parker, Totterdell, & Hagger-Johnson, 2012; Frese & Fay, 2001; Grant & Ashford, 2008).

In this chapter we examine some possibilities of using computer simulation methods to model the interaction of affect and cognition in organizations, with a particular focus on agent-based modeling (ABM) techniques. Our chapter has two main aims. First, we take stock of methodological progress in this area, highlighting important developments in the modeling of affect and cognition in other fields, including psychology and economics. Second, we outline how ABM in particular can help to advance managerial and organizational cognition by building and testing theoretical models predicated on the interaction of affect and cognition. We argue that using ABM for this purpose can improve the level of specificity of cognitive and affective concepts and their interrelationships in organizational theories, yield more behaviorally plausible models of behavior in and of organizations, and deepen understanding of the generative behavioral mechanisms of multi-level organizational phenomena. We highlight possibilities for using ABM to model affect–cognition interactions in studies of mental models, collective cognition, diversity in work groups and teams, and organizational decision-making.

Most network research in organizations assumes away the dissociative forces instantiated in negative ties, instead pursuing ties that reflect only associative forces, to the detriment of understanding organizational networks. This essay provides a brief history of negative tie research in organizations; discusses different definitions of negative ties, situating them within the tripartite model of interpersonal attitudes; suggests alternative paths to network dynamics when considering negative ties; covers existing and suggested paths to studying personality antecedents of negative ties; and briefly reviews the research on the consequences of negative ties in organizations and suggestions for future work.