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The game itself?
(2020)
In this paper, we reassess the notion and current state of ludohermeneutics in game studies, and propose a more solid foundation for how to conduct hermeneutic game analysis. We argue that there can be no ludo-hermeneutics as such, and that every game interpretation rests in a particular game ontology, whether implicit or explicit. The quality of this ontology, then, determines a vital aspect of the quality of the analysis.
The game itself?
(2020)
In this paper, we reassess the notion and current state of ludohermeneutics in game studies, and propose a more solid foundation for how to conduct hermeneutic game analysis. We argue that there can be no ludo-hermeneutics as such, and that every game interpretation rests in a particular game ontology, whether implicit or explicit. The quality of this ontology, then, determines a vital aspect of the quality of the analysis.
The envy spiral
(2020)
On Social Networking Sites (SNS) users disclose mostly positive and often self-enhancing information. Scholars refer to this phenomenon as the positivity bias in SNS communication (PBSC). However, while theoretical explanations for this phenomenon have been proposed, an empirical proof of these theorized mechanisms is still missing. The project presented in this Research-in-Progress paper aims at explaining the PBSC with the mechanism specified in the self-enhancement envy spiral. Specifically, we hypothesize that feelings of envy drive people to post positive and self-enhancing content on SNS. To test this hypothesis, we developed an experimental design allowing to examine the causal effect of envy on the positivity of users’ subsequently posted content. In a preliminary study, we tested our manipulation of envy and could show its effectiveness in inducing different levels of envy between our groups. Our project will help to broaden the understanding of the complex dynamics of SNS and the potentially adverse driving forces underlying them.
In the time of digitalization the demand for organizational change is rising and demands ways to cope with fundamental changes on the organizational as well as individual level. As a basis, learning and forgetting mechanisms need to be understood in order to guide a change process efficiently and successfully. Our research aims to get a better understanding of individual differences and mechanisms in the change context by performing an experiment where individuals learn and later re-learn a complex production process using a simulation setting. The individual’s performance, as well as retentivity and prior knowledge is assessed. Our results show that higher retentivity goes along with better learning and forgetting performances. Prior knowledge did not reveal such relation to the learning and forgetting performances. The influence of age and gender is discussed in detail.
Public blockchain
(2020)
Blockchain has the potential to change business transactions to a major extent. Thereby, underlying consensus algorithms are the core mechanism to achieve consistency in distributed infrastructures. Their application aims for transparency and accountability in societal transactions. As a result of missing reviews holistically covering consensus algorithms, we aim to (1) identify prevalent consensus algorithms for public blockchains, and (2) address the resource perspective with a sustainability consideration (whereby we address the three spheres of sustainability). Our systematic literature review identified 33 different consensus algorithms for public blockchains. Our contribution is twofold: first, we provide a systematic summary of consensus algorithms for public blockchains derived from the scientific literature as well as real-world applications and systemize them according to their research focus; second, we assess the sustainability of consensus algorithms using a representative sample and thereby highlight the gaps in literature to address the holistic sustainability of consensus algorithms.
Data sharing requires researchers to publish their (primary) data and any supporting research materials. With increased attention on reproducibility and more transparent research requiring sharing of data, the issues surrounding data sharing are moving beyond whether data sharing is beneficial, to what kind of research data should be shared and how. However, despite its benefits, data sharing still is not common practice in Information Systems (IS) research. The panel seeks to discuss the controversies related to data sharing in research, specifically focusing on the IS discipline. It remains unclear how the positive effects of data sharing that are often framed as extending beyond the individual researcher (e.g., openness for innovation) can be utilized while reducing the downsides often associated with negative consequences for the individual researcher (e.g., losing a competitive advantage). To foster data sharing practices in IS, the panel will address this dilemma by drawing on the panelists’ expertise.