Tim Schürmann, Nina Gerber and Paul Gerber
Online privacy research has seen a focus on user behavior over the last decade, partly to understand and explain user decision-making and seeming inconsistencies regarding users'…
Abstract
Purpose
Online privacy research has seen a focus on user behavior over the last decade, partly to understand and explain user decision-making and seeming inconsistencies regarding users' stated preferences. This article investigates the level of modeling that contemporary approaches rely on to explain said inconsistencies and whether drawn conclusions are justified by the applied modeling methodology. Additionally, it provides resources for researchers interested in using computational modeling.
Design/methodology/approach
The article uses data from a pre-existing literature review on the privacy paradox (N = 179 articles) to identify three characteristics of prior research: (1) the frequency of references to computational-level theories of human decision-making and perception in the literature, (2) the frequency of interpretations of human decision-making based on computational-level theories, and (3) the frequency of actual computational-level modeling implementations.
Findings
After excluding unrelated articles, 44.1 percent of investigated articles reference at least one theory that has been traditionally interpreted on a computational level. 33.1 percent of all relevant articles make statements regarding computational properties of human cognition in online privacy scenarios. Meanwhile, 5.1 percent of all relevant articles apply formalized computational-level modeling to substantiate their claims.
Originality/value
The findings highlight the importance of formal, computational-level modeling in online privacy research, which has so far drawn computational-level conclusions without utilizing appropriate modeling techniques. Furthermore, this article provides an overview of said modeling techniques and their benefits to researchers, as well as references for model theories and resources for practical implementation.
Details
Keywords
This study aims to determine the distance and duration to reach airports mixing height of 3,000 feet limit. Airport operations significantly contribute to the aircraft landing and…
Abstract
Purpose
This study aims to determine the distance and duration to reach airports mixing height of 3,000 feet limit. Airport operations significantly contribute to the aircraft landing and take-off (LTO) cycle. Eurocontrol’s SO6 data sets comprise several abutted segment data to analyse the duration and distance for specific flights.
Design/methodology/approach
Two consequential methods have been used to calculate the distance and destination from the SO6 databases. First, SQL filtering and pivot tables were formed for the required data. Second, over 583,000 data lines for a year of Boeing 747–400 aircraft routes were calculated and filtered for the monthly assessments.
Findings
LTO cycles’ durations have deviated −24% to 76% from the ICAO assumptions. Distance facts determined for specific airports as 2.57 to 3.66 nm for take-off and 5.02 to 23.25 nm for the landing. The average duration of the aircraft’s in mentioned airport take-off are 66 to 74 s and 40 to 50 s; averages have been calculated as 70 to 44 s. Landing durations have been calculated for four different airports as 173 to 476 s.
Practical implications
This study provides a re-evaluation chance for the current assumptions and helps for better assessments. Each airport and aircraft combinations have their duration and distance figures.
Originality/value
This study has calculated the first LTO distances in the literature for the aerodrome. This method applies to all airports, airline fleets and aircraft if the segmented SO6 data are available.
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Joris van Ruysseveldt, Tonnie van Wiggen-Valkenburg and Karen van Dam
The purpose of this study is to develop the self-initiated work adjustment for learning (SIWAL) scale that measures the adjustments that employees make in their work to enhance…
Abstract
Purpose
The purpose of this study is to develop the self-initiated work adjustment for learning (SIWAL) scale that measures the adjustments that employees make in their work to enhance learning, based on theories and research on workplace learning, work adjustment and work design.
Design/methodology/approach
The SIWAL scale was validated in two independent studies. Study 1 (n = 208) focused on the internal consistency and factor structure of the SIWAL scale. Study 2 (n = 178) re-examined the factorial structure using confirmatory factor analysis and investigated scale validity.
Findings
In both studies, the SIWAL scale showed good psychometric characteristics, i.e. a clear two-factorial structure and internal reliable sub-scales. The findings also indicated convergent, divergent and concurrent validity.
Research limitations/implications
Using the SIWAL scale, future research could focus on the individual, social and organizational predictors and outcomes of SIWAL, collect supervisor and peer ratings to further validate this self-report scale and investigate lower-educated workers.
Practical implications
Organizations might try to enhance their employees' SIWAL through organizational policies, such as supportive leadership, and a learning climate.
Originality/value
This study provides a first step toward a better understanding of what workers do to enhance their workplace learning. The study findings indicate that employees address two adaptive behaviors: adjusting job responsibilities and adjusting social interactions.
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Coventry‐based Shipley Europe have appointed George Allardyce to the newly‐created position of European technical manager. The appointment further strengthens Shipley's research…
Abstract
Coventry‐based Shipley Europe have appointed George Allardyce to the newly‐created position of European technical manager. The appointment further strengthens Shipley's research, development and technical support team serving the European marketplace.