TY - CHAP ED - Czarnecki, Christian ED - Lübbe, Alexander ED - Meister, Vera G. ED - Müller, Christian ED - Steglich, Mike ED - Walther, Mathias T1 - Angewandte Forschung in der Wirtschaftsinformatik 2023 : Tagungsband zur 36. AKWI-Jahrestagung vom 11.09.2023 bis 13.09.2023 ausgerichtet von der Technischen Hochschule Wildau N2 - Tagungsband zur 36. Jahrestagung des "Arbeitskreises Wirtschaftsinformatik an Hochschulen für Angewandte Wissenschaften im deutschsprachigen Raum" (AKWI) der Gesellschaft für Informatik e. V. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17940 ER - TY - JOUR A1 - Belcheva, Veronika A1 - Ermakova, Tatiana A1 - Fabian, Benjamin T1 - Understanding Website Privacy Policies—A Longitudinal Analysis Using Natural Language Processing JF - Information N2 - Privacy policies are the main method for informing Internet users of how their data are collected and shared. This study aims to analyze the deficiencies of privacy policies in terms of readability, vague statements, and the use of pacifying phrases concerning privacy. This represents the undertaking of a step forward in the literature on this topic through a comprehensive analysis encompassing both time and website coverage. It characterizes trends across website categories, top-level domains, and popularity ranks. Furthermore, studying the development in the context of the General Data Protection Regulation (GDPR) offers insights into the impact of regulations on policy comprehensibility. The findings reveal a concerning trend: privacy policies have grown longer and more ambiguous, making it challenging for users to comprehend them. Notably, there is an increased proportion of vague statements, while clear statements have seen a decrease. Despite this, the study highlights a steady rise in the inclusion of reassuring statements aimed at alleviating readers’ privacy concerns. KW - privacy policy KW - longitudinal analysis KW - text analysis KW - NLP KW - readability KW - vagueness Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18268 SN - 2078-2489 VL - 14 IS - 11 PB - MDPI ER - TY - JOUR A1 - Roedenbeck, Marc A1 - Poljsak-Rosinski, Petra T1 - Artificial neural network in soft HR performance management: new insights from a large organizational dataset JF - Evidence-based HRM N2 - Purpose This study investigates whether the artificial neural network approach, when used on a large organizational soft HR performance dataset, results in a better (R2/RMSE) model compared to the linear regression. With the use of predictive modelling, a more informed base for managerial decision making within soft HR performance management is offered. Design/methodology/approach The study builds on a dataset (n > 43 k) stemming from an annual employee MNC survey. It covers several soft HR performance drivers and outcomes (such as engagement, satisfaction and others) that either have evidence of a dual-role nature or non-linear relationships. This study applies the framework for artificial neural network analysis in organization research (Scarborough and Somers, 2006). Findings The analysis reveals a substantial artificial neural network model performance (R2 > 0.75) with an excellent fit statistic (nRMSE <0.10) and all drivers have the same relative importance (RMI [0.102; 0.125]). This predictive analysis revealed that the organization has to increase six of the drivers, keep two on the same level and decrease one. Originality/value Up to date, this study uses the largest dataset in soft HR performance management. Additionally, the predictive results reveal that specific target values lay below the current levels to achieve optimal performance. KW - soft HRM KW - performance KW - drivers KW - artificial neural network KW - non-linearity KW - prediction Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18377 SN - 2049-3991 VL - 11 IS - 3 SP - 519 EP - 537 PB - Emerald ER -