TY - CHAP A1 - Gliem, Silvia A1 - Hipp, Christiane ED - Tronvoll, Bård ED - Edvardsson, Bo T1 - Service Productivities’ Next Top Model T2 - The Palgrave Handbook of Service Management Y1 - 2022 SN - 978-3-030-91827-9 SN - 978-3-030-91830-9 SN - 978-3-030-91828-6 U6 - https://doi.org/10.1007/978-3-030-91828-6_37 SP - 773 EP - 799 PB - Springer Nature Switzerland CY - Schweiz ER - TY - CHAP A1 - Magnusson, Peter A1 - Hipp, Christiane A1 - Edvardsson, Bo ED - Kristensson, Per ED - Magnusson, Peter ED - Witell, Lars T1 - Exploring the Challenges of Servitization in Manufacturing Companies T2 - Service innovation for sustainable business : stimulating, realizing and capturing the value from service innovation Y1 - 2019 SN - 978-981-3273-37-5 SP - 253 EP - 279 PB - World Scientific CY - London ER - TY - GEN A1 - Alkhasli, Isabel A1 - Hoffmann, Christin T1 - Neural and social-psychological correlates of immersion in a virtual reality safety training. T2 - Proceedings of the third Neuroadaptive Technologie Conference 2022 Y1 - 2022 UR - https://neuroadaptive.org/wp-content/uploads/2022/10/NAT22_Programme.pdf SP - 115 EP - 117 ER - TY - GEN A1 - Alkhasli, Isabel A1 - Mottaghy, Felix M. A1 - Binkofski, Ferdinand A1 - Sakreida, Katrin T1 - Preconditioning prefrontal connectivity using transcranial direct current stimulation and transcranial magnetic stimulation T2 - Frontiers in human neurosciences Y1 - 2022 U6 - https://doi.org/10.3389/fnhum.2022.929917 SN - 1662-5161 VL - Vol. 16 SP - 1 EP - 14 ER - TY - THES A1 - Alkhasli, Isabel T1 - Modulating resting state functional connectivity of the dorsolateral prefrontal cortex using transcranial magnetic stimulation and transcranial direct current stimulation Y1 - 2022 N1 - Rheinisch-Westfälische Technische Hochschule Aachen CY - Aachen ER - TY - GEN A1 - Konlechner, Stefan A1 - Keller, Arne A1 - Martin, Alexander A1 - Güttel, Wolfgang A1 - Fortwengel, Johann T1 - Sequential leadership logics for coping with tensions in structurally ambidextrous units T2 - Forschungspapier mit Vortrag bei der Konferenz 39th EGOS Colloquium 2023, 6.-8. Juli, Cagliari (Italien) Y1 - 2023 UR - https://www.egos.org/jart/prj3/egos/main.jart?rel=de&reserve-mode=active&content-id=1658501434997&subtheme_id=1633951214485&show_prog=yes ER - TY - GEN A1 - Keller, Arne A1 - Martin, Alexander A1 - Konlechner, Stefan A1 - Güttel, Wolfgang A1 - Fortwengel, Johann T1 - Evolving Tensions Across Different Stages of Ambidexterity T2 - Academy of Management Proceedings Y1 - 2023 U6 - https://doi.org/10.5465/AMPROC.2023.17856abstract SN - 0065-0668 SN - 2151-6561 VL - 2023 IS - 1 ER - TY - GEN A1 - Städter, Christian A1 - Raetze, Sebastian T1 - Effectiveness in esports : an integrative multilevel review T2 - Esports Research Network Conference 2024 N2 - Introduction: Esports have evolved from unorganized activities into hyper-competitive and professionalized settings. Esports share similarities with professional sports where individuals and teams strive for optimal performance. The purpose of our paper is to systematically review and integrate the fast-growing research on individual and team effectiveness in gaming and esports. To the best of our knowledge, we provide the first systematic review of research on gaming and esports effectiveness. Research Question: What are the individual, team, and organizational/environmental factors that are associated with effectiveness in gaming and esports? Methodology: We leveraged a systematic literature review approach and conducted a structured keyword search in Web of Science, Google Scholar, and EBSCO. The sample was completed using forward-backward tracking. The resulting sample consists of 180 articles after full-text screening. Results: Within our full paper we will leverage the input-mediator-outcome-input (IMOI) model to synthesize the existing findings on the multilevel factors that shape individual and team effectiveness in gaming and esports. In doing so, we will integrate previous findings from across the individual, team, and organizational/environmental levels into outcomes (e.g., win/loss ratio and prize money winnings), mediators (e.g., team cohesion and communication), and inputs (e.g., individual athlete skills, team compositional variables). Building on our integration, we 2 will discuss blind spots and contradicting findings within this model, and outline an agenda for future research. Implications: Scholars can leverage our work by systematically targeting the identified blind spots and contradicting findings that exist within the literature. Practitioners will find value in focusing on factors shown to positively impact effectiveness within this setting. KW - Effectiveness KW - Performance KW - Video Games KW - Gaming KW - Literature Review Y1 - 2024 UR - https://www.researchgate.net/publication/389079264_EFFECTIVENESS_IN_ESPORTS_AN_INTEGRATIVE_MULTILEVEL_REVIEW PB - Research Gate ER - TY - GEN A1 - Städter, Christian A1 - Bennett, Kyle A1 - Poulus, Dylan A1 - Leis, Oliver A1 - Sharpe, Benjamin T. A1 - Nicholson, Mitchell T1 - Reporting esports samples : a scoping review T2 - Esports Research Network Conference 2024 N2 - Introduction: The growth of gaming and esports has spurred increased academic interest and research across various disciplines. However, the relative novelty of this field means that best practices are not yet established, leading to inconsistent and inadequate sample information. This paper systematically reviews the existing literature, examining how researchers report esports samples, and addresses gaps identified in previous reviews, such as Mendoza et al. (2023). Research Questions: What descriptive information is provided when reporting esports samples? What categories have been adopted to date when addressing such samples? Methodology: We leveraged a scoping review approach following PRISMA-ScR guidelines. We conduct a structured keyword search in databases (Web of Science, EBSCOhost, Google Scholar, and Science Direct) across multiple disciplines in Mai 2024, using the PICO model. In our review, only peer-reviewed, primary, empirical, English studies will be included. 2 Results: The search provided an initial sample consisting of 8,536 articles before screening. Within our full paper we will, we will report main study outcomes, characteristics of all relevant literature, and highlight emerging trends. Discussion: We will discuss key outcomes such as quality and quantity of information as well as terms used by researchers based on the literature. Furthermore, strengths and limitations, as well as current practices and lack thereof will be highlighted. Lastly, we will suggest guidance for future research regarding esports sampling. KW - Esports KW - Scoping Review KW - Sampling KW - Methodology KW - Electronic Gaming Y1 - 2024 UR - https://www.researchgate.net/publication/389081294_REPORTING_ESPORTS_SAMPLES_A_SCOPING_REVIEW PB - Research Gate ER - TY - GEN A1 - Raetze, Sebastian A1 - Hüllmann, Joschka A1 - Städter, Christian T1 - Not all adversity is equal: Analyzing the impact of different trigger types on team coordination and performance based on digital trace data from professional esports T2 - Interdisciplinary Network for Group Research (INGRoup) 2024 N2 - Teams are prevalent across various industries, undertaking a significant portion of contemporary work (Benson-Armer & Hsieh, 1997). Good functioning of teams is paramount, given its pivotal role in enhancing productivity and fostering corporate success (LePine, 2005). As such, organizational teamwork stands as a widely explored subject, attracting attention from both the popular press and academia (Duhigg, 2016). In the academic realm, the primary aim is to understand the drivers of team effectiveness, with most studies employing frameworks such as the input-process-outcome model (McGrath, 1984) or the advanced input-mediator-outcome model (Ilgen et al., 2005), which cluster variables related to effective teamwork into process categories. For instance, team research has long focused on compositional factors as inputs, revealing the significant influence that factors such as personality, knowledge, and skills of team members exert on team processes and performance (e.g., Mathieu et al., 2008; 2019). While valuable, much of the existing research on organizational teams has adopted a static approach, focusing on teams performing routine tasks at specific moments in time (e.g., Cronin et al., 2011; Klonek et al., 2019). However, this approach fails to capture the dynamic nature of most teams operating today, which are fluid entities that evolve and adapt over time (e.g., Ramos-Villagrasa et al., 2018). Consequently, the challenges a team faces at one point may not remain stable throughout its entire lifecycle. Events or disruptions can profoundly influence a team’s functioning and performance (Liu et al., 2023). Likewise, a team’s past experiences can shape its ability to navigate present disruptions. This is even more pertinent in today’s era of 28 discontinuity and perpetual change where most teams frequently encounter challenges that demand immediate action and have the potential to disrupt critical team processes and states (e.g., Alliger et al., 2015; Driskell et al., 2018). Due to these developments, there has been an exponential increase in research on teamwork in challenging contexts, encompassing studies on various fronts such as extreme teams (Schmutz et al., 2023), team resilience (Raetze et al., 2021), and team adaptation (e.g., Maynard et al., 2015). However, even within these domains, there has been limited emphasis on trigger events and temporal dynamics. For instance, while many definitions of team resilience acknowledge adversity as a trigger and imply temporal effects like recovery, they often operationalize team resilience as a collective state at a particular moment in time (Raetze et al., 2022). Although the team adaptation literature demonstrates a stronger focus on how trigger types influence team processes and outcomes (Rico et al., 2019; 2020), empirical evidence remains sparse (Christian et al., 2017). Furthermore, the existing empirical work investigating how teams adapt to adversity has predominantly centered on disruptions occurring before or between different task execution rounds (e.g., Argote et al., 1995; Kane et al., 2005). Consequently, we argue that research elucidating the role of adversity in effective teamwork remains at a nascent stage (Edmondson & McManus, 2007). In response, in our study, we aim to provide an in-depth exploration of how various types of adverse events influence team routines and performance, with a specific focus on the role of temporal variables. To achieve this, we utilize high-resolution data obtained from the innovative setting of professional esports. Professional esports represents a dynamic and demanding environment where athletes or teams compete for substantial prize money. Our analysis concentrates on demo files from Counter-Strike:Global Offensive (CS:GO), a team-based first-person shooter game, which comprise 41,782 unique team performance episodes. From these 29 demos, we extract digital trace data, capturing events and behaviors 128 times per second. Employing methodologies rooted in Business Process Management (BPM) and Process Mining (van der Aalst, 2016), we will analyze this rich process data. Our analytical framework draws on established management theories such as EST (Morgeson et al., 2015), theories on team dynamics (e.g., Arrow & McGrath, 1993; O'Leary et al., 2011), and insights from research on team resilience and adaptation (e.g., Rico et al., 2019; 2020; Stoverink et al., 2020). Study Context and Methodological Approach We searched for a context that offers high-resolution data (Klonek et al., 2019) on adverse events and process dynamics in real teams and found it in the domain of professional esports. Esports in general have evolved from recreational/unorganized activities into hyper-competitive/ professionalized settings (Pedraza-Ramirez, 2020). Professional athletes play full-time, earn a salary, and compete in tournaments and leagues with millions of dollars in prize money (Chan et al., 2022). In particular, we focus on the first-person shooter CS:GO, which is one of the most popular esports titles. Professional tournaments/leagues in this domain have structures and rules similar to traditional sports. In a game of CS:GO, two teams of five compete against each other. Each game consists of multiple rounds. Both teams move across a 3D map during a round (see Figure 2-1). The attacking team must plant and detonate a bomb, whereas the defending team tries to stop this from happening. At the beginning of each round, all players start with 100 health points, and each player can acquire weapons and utilities of varying strength and cost by spending money to fight against the opponents. Money transitions across rounds, with each new round giving money to both teams. Weapon and utilities transition only across rounds if a player survives. Damage can be inflicted on players via weapons or utilities. When a player reaches 0 health points, the player dies and is revived at the start of the next round. The team that first accumulates 16 winning rounds wins the game. Each round can be considered a team 30 performance episode (Marks et al., 2001). Due to the aforementioned nature of modern esports, CS:GO teams at the highest levels can be compared to professional sports teams, which have a rich tradition in work teams research (Quigley et al., 2022), but also to critical action teams (Ishak & Ballard, 2012). Yet, esports offers much richer and more fine-grained data than professional sports, which offers scholars the opportunity to analyze team process dynamics based on unobtrusive measures. For instance, during major CS:GO tournaments players and teams are video- and audio-recorded as are comments of several experts. Moreover, statistics and tournament demos are published on community websites. Our study focuses on demo files from professional CS:GO tournaments in 2021 and 2022. Each demo constitutes a digital trace data file of one game and grants access to the entirety of a team’s behavioral data during this game (e.g., events, player’s position, movement, and action). Digital trace data allow for including fine-grained contextual information in the description of processes (Pentland et al., 2020) and has gained momentum in management and team research recently (e.g., Hansson et al., 2022; Wax et al., 2017). In particular, we acquired the esports trajectories and actions (ESTA) dataset comprising 1,558 replays with 41,782 rounds played accounting for 8.6 million game events (Xenopoulos & Silva, 2022). The events are accompanied by more than 110 attributes, describing the game state at each 1/128 fraction of a second (as professional games are played at 128 frames per second with each frame representing the entire game state at this particular point in time). Current State and Next Steps Using high-resolution data to understand team dynamics is challenging. As such, team scholars have seldomly applied such approaches and there is also limited methodological guidance (Klonek et al., 2019). We know even less about how to leverage digital traces as a particularly complex type of high-resolution data (Mahringer, 2021; Pentland et al., 2020). 31 Accordingly, such research requires a more exploratory and iterative approach (Uitdewilligen & Waller, 2018). For instance, for assigning our data to meaningful team concepts, we have to go back and forth between theory and data to test which concepts can be operationalized and which cannot (Klonek et al., 2019). Likewise, the extremely high resolution of our data (i.e., 128 measurement points per second) creates very high complexity which calls for simplification. At this stage of our project, we have created an initial set of operationalizations for events, team routines, and team performance within single team performance episodes (see Table 2-1). For instance, we operationalize adverse trigger events through in-game events such as damage, kill, or bomb planted against the focal team. These critical events significantly reduce the focal team’s chance of winning a respective round (Xenopoulos et al., 2020). We operationalize team routines as the joint trajectory of team players’ movements in the x- and y-dimensions throughout the round. For this, we reduced the 3D game state to a 2D game state, only considering variation in the horizontal and vertical axes (Gust et al., 2023). We thus ignored the 3D viewpoint of the players (i.e., where they are looking in-game) and only considered their positions (i.e., where they are standing or moving in-game). The team resources and team situations, which determine the choices of team routines, are operationalized via the game factors of equipment (i.e., the value of the team’s in-game equipment such as weapons, grenades, and defusal kits) and the remaining total health of all players. These factors are also significantly related to the round outcomes (Xenopoulos et al., 2020; 2022). Finally, the round outcomes (i.e., round win and total damage inflicted on the opponent’s team) operationalize the team performance for that respective round. Likewise, we follow an analytical approach to determine the observed routines of a focal team based on the “x,y” trajectories of its players. Figure 2-2 illustrates the “x,y” trajectories as measures of team routines. Using hierarchical clustering and manual labeling, we define types of team routines that are semantically viable (Hu et al., 2023; Studer & Ritschard, 2016). 32 Subsequently, in further analysis, the observed routines are assigned to one of the defined routine types via maximizing trajectory similarity (minimizing distance). We use routine types and repertoires as predictors for team performance. Over the next weeks, we will finalize our analytical model and run our analyses. We are currently discussing/testing the operationalization of additional adversity-trigger characteristics (e.g., event time, event cluster/chains), types of impacts on team routines (e.g., collapse of routines, improvisation, sticking to a routine, switching to another routine), and alternative outcome measures (e.g., resilient performance) as well as options to analyze interrelations across single performance episodes (i.e., adding a developmental perspective; Klonek et al., 2019). During the conference, we will present our initial findings. Expected Findings and Conclusions Overall, we are very confident that our work will offer valuable insights into how accounting for adversity-trigger characteristics may shed new light on the consequences of adversity exposure on team processes and performance as well as adequate adversity management processes. Our fine-grained data will also allow us to take several temporal characteristics into account. This knowledge is strongly needed to support teams operating in today’s complex environments. KW - Esports KW - Adversity KW - Team KW - Routine KW - Performance KW - Digital Trace Data Y1 - 2024 UR - https://www.researchgate.net/publication/389082275_Not_all_adversity_is_equal_Analyzing_the_impact_of_different_trigger_types_on_team_coordination_and_performance_based_on_digital_trace_data_from_professional_esports SP - 23 PB - Research Gate ER -