This paper proposes and showcases a methodology to develop an observational behavior assessment instrument to assess psychological competencies of police officers. We outline a step-by-step methodology for police organizations to measure and evaluate behavior in a meaningful way to assess these competencies. We illustrate the proposed methodology with a practical example. We posit that direct behavioral observation can be key in measuring the expression of psychological competence in practice, and that psychological competence in practice is what police organizations should care about. We hope this paper offers police organizations a methodology to perform scientifically informed observational behavior assessment of their police officers’ psychological competencies and inspires additional research efforts into this important area.
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With the development of Enterprise Architecture (EA) as a discipline, measuring and understanding its value for business and IT has become relevant. In this paper a framework for categorizing the benefits of EA, the Enterprise Architecture Value Framework (EAVF), is presented and based on this framework, a measurability maturity scale is introduced. In the EAVF the value aspects of EA are expressed using the four perspectives of the Balanced Scorecard with regard to the development of these aspects over time, defining sixteen key areas in which EA may provide value. In its current form the framework can support architects and researchers in describing and categorizing the benefits of EA. As part of our ongoing research on the value of EA, two pilots using the framework have been carried out at large financial institutions. These pilots illustrate how to use the EAVF as a tool in measuring the benefits of EA
Background: To experience external objects in such a way that they are perceived as an integral part of one's own body is called embodiment. Wearable technology is a category of objects, which, due to its intrinsic properties (eg, close to the body, inviting frequent interaction, and access to personal information), is likely to be embodied. This phenomenon, which is referred to in this paper as wearable technology embodiment, has led to extensive conceptual considerations in various research fields. These considerations and further possibilities with regard to quantifying wearable technology embodiment are of particular value to the mobile health (mHealth) field. For example, the ability to predict the effectiveness of mHealth interventions and knowing the extent to which people embody the technology might be crucial for improving mHealth adherence. To facilitate examining wearable technology embodiment, we developed a measurement scale for this construct. Objective: This study aimed to conceptualize wearable technology embodiment, create an instrument to measure it, and test the predictive validity of the scale using well-known constructs related to technology adoption. The introduced instrument has 3 dimensions and includes 9 measurement items. The items are distributed evenly between the 3 dimensions, which include body extension, cognitive extension, and self-extension.Methods: Data were collected through a vignette-based survey (n=182). Each respondent was given 3 different vignettes, describing a hypothetical situation using a different type of wearable technology (a smart phone, a smart wristband, or a smart watch) with the purpose of tracking daily activities. Scale dimensions and item reliability were tested for their validity and Goodness of Fit Index (GFI). Results: Convergent validity of the 3 dimensions and their reliability were established as confirmatory factor analysis factor loadings45 (>0.70), average variance extracted values40 (>0.50), and minimum item to total correlations50 (>0.40) exceeded established threshold values. The reliability of the dimensions was also confirmed as Cronbach alpha and composite reliability exceeded 0.70. GFI testing confirmed that the 3 dimensions function as intercorrelated first-order factors. Predictive validity testing showed that these dimensions significantly add to multiple constructs associated with predicting the adoption of new technologies (ie, trust, perceived usefulness, involvement, attitude, and continuous intention). Conclusions: The wearable technology embodiment measurement instrument has shown promise as a tool to measure the extension of an individual's body, cognition, and self, as well as predict certain aspects of technology adoption. This 3-dimensional instrument can be applied to mixed method research and used by wearable technology developers to improve future versions through such things as fit, improved accuracy of biofeedback data, and customizable features or fashion to connect to the users' personal identity. Further research is recommended to apply this measurement instrument to multiple scenarios and technologies, and more diverse user groups.
