The Role of Information Elaboration for Co-Construction of Meaning during Idea Convergence: A Causal Mediation Analysis

Teams need to co-construct meaning to establish shared understanding about concepts when converging on the best ideas generated from crowdsourcing events. Facilitation interventions can aid the co-construction of meaning. The causal mechanism is believed to be the extent exchanged information is elaborated on. However, this mediating role has not been empirically confirmed in past research. Information elaboration in teams with and without facilitation intervention was tested with causal mediation analysis by drawing on data collected in a laboratory experiment. The findings suggest that facilitated teams had better information elaboration and co-construction than non-facilitated teams. Moreover, information elaboration could be identified as a strong causal mechanism through which facilitation interventions affect the co-construction of meaning. The study contributes to unravelling the black box of team processes through which this causal effect of facilitation intervention arises and helps fostering the design of improved automated feedback mechanisms

Working from Home with Flexible and Permeable Boundaries

The COVID-19 pandemic forced information workers across the world to work from home. This situation removes the physical boundary between work and home, impacting their work-life balance. How information workers configure the digital workplace (DWP) to manage their workplace boundaries and what effect this has on their individual job satisfaction remains unclear. To close this gap in the literature, 202 information workers completed an online survey. The findings partially confirm existing theory that more work flexibility increases job satisfaction while more work permeability decreases job satisfaction. However, depending on the flexibility and permeability of their work-home boundaries, the frequency with which information workers use DWP tools has cross-over effects on job satisfaction. The findings contribute to boundary theory and the new stream of digital workplace literature

Human-Robot Interaction: Mapping Literature Review and Network Analysis

Organizations increasingly adopt social robots as additions to real-life workforces, which requires knowledge of how humans react to and work with robots. The longstanding research on Human-Robot Interaction (HRI) offers relevant insights, but the existing literature reviews are limited in their ability to guide theory development and practitioners in sustainably employing social robots because the reviews lack a systematic synthesis of HRI concepts, relationships, and ensuing effects. This study offers a mapping review of the past ten years of HRI research. With the analysis of 68 peer-reviewed journal articles, we identify shifting foci, for example, towards more application-specific empirical investigations, and the most prominent concepts and relationships investigated in connection with social robots, for example, robot appearance. The results offer Information Systems scholars and practitioners an initial knowledge base and nuanced insights into key predictors and outcome variables that can hinder and foster social robot adoption in the workplace

Comparing Pineapples with Lilikois: An Experimental Analysis of the Effects of Idea Similarity on Evaluation Performance in Innovation Contests

Identifying promising ideas from large innovation contests is challenging. Evaluators do not perform well when selecting the best ideas from large idea pools as their information processing capabilities are limited. Therefore, it seems reasonable to let crowds evaluate subsets of ideas to distribute efforts among the many. One meaningful approach to subset creation is to draw ideas into subsets according to their similarity. Whether evaluation based on subsets of similar ideas is better than compared to subsets of random ideas is unclear. We employ experimental methods with 66 crowd workers to explore the effects of idea similarity on evaluation performance and cognitive demand. Our study contributes to the understanding of idea selection by providing empirical evidence that crowd workers presented with subsets of similar ideas experience lower cognitive effort and achieve higher elimination accuracy than crowd workers presented with subsets of random ideas. Implications for research and practice are discussed

IT Enablers for Task Organization and Innovation Support to drive Team Performance

Teams drive organizational innovation by applying knowledge to solve complex problems. However, many teams underperform and organizations do not sufficiently harvest the benefits they could gain from effective IT support for team processes consisting of creative (exploration) and structurally controlled (exploitation) processes. This paper investigates the impact of knowledge application on support for innovation, task organization, and team performance in a mixed method case study in two medium-sized, knowledge-intensive, information technology-affine organizations. We surveyed 204 employees and found that knowledge application positively affects task organization. Knowledge application and task organization positively affect support for innovation. Both, task organization and support for innovation positively affect team performance. Subsequent focus group interviews with 16 employees provided us with an in-depth understanding of factors that support team performance. Qualitative content analysis resulted in nine IT enablers, which can be adapted by organizations to foster coordination while at the same time promote innovation

How Digital Nudges Affect Consideration Set Size and Perceived Cognitive Effort in Idea Convergence of Open Innovation Contests

Open innovation initiatives are useful to acquire many ideas, but often face problems when it comes to selecting the best ideas. Idea convergence has been suggested as a first step in idea selection to filter those ideas that are worthy of further consideration. Digital nudges – digital interventions that aim at altering human behavior in a predictable way - could support convergence. However, their effects are largely unknown. This study explores how two digital nudges, selection strategy (inclusion/exclusion) and idea subset similarity (similar/random), affect the convergence outcomes consideration set size and perceived cognitive effort. We conducted a laboratory experiment with 88 students and found that guiding individuals towards an inclusion strategy results in smaller consideration sets and higher perceived cognitive effort. Moreover, presenting individuals with subsets of similar ideas resulted in smaller consideration sets. These insights are relevant for the design and use of digital nudges for convergence in open innovation environments

Designing a Digital Nudge for Convergence: The Role of Decomposition of Information Load for Decision Making and Choice Accuracy

Innovation contests offer organizations the opportunity to source innovative ideas to achieve competitive advantage. However, raters cannot easily converge on the most promising ideas because they can easily feel overwhelmed by the high number of generated ideas. Further, information overload will likely impair raters’ decision-making processes and how well they can accurately distinguish good from bad ideas. Digital nudging may counteract this convergence challenge via user interface elements to change how information is presented to users. To design a digital nudge in a convergence platform to effectively nudge raters towards improved choice accuracy, one needs to understand the decision-making processes associated with the convergence task. Considering this goal, we conducted an online experiment in which 190 participants eliminated the least promising ideas in presentation modes with either a high (two ideas/screen) or low (30 ideas/screen) decomposition of information load. Our findings suggest that convergence platforms with a high decomposition of information load help raters make more accurate choices. The extent of elimination and revision decisions raters make partially explained this effect. However, these paradoxical mediation effects depended on whether raters showed a high or low tendency to follow the crowd’s opinion. Our findings add to the growing academic knowledge base on idea-selection processes and how one can design convergence platforms with digital nudges to help raters deal with their cognitive constraints and ensure successful convergence

How do Pedagogical Conversational Agents affect Learning Outcomes among High School Pupils: Insights from a Field Experiment

Pedagogical conversational agents (CA) support formal and informal learning to help students achieve better learning outcomes by providing information, guidance or fostering reflections. Even though the extant literature suggests that pedagogical CAs can improve learning outcomes, there exists little empirical evidence of what design features drive this effect. This study reports on an exploratory field experiment involving 31 pupils in commercial high schools and finds that students achieved better learning outcomes when preparing for their tests with a pedagogical CA than without. However, the drivers of this effect remain unclear. Neither the use frequency of the design features nor the pupils’ expectations towards the CA could explain the improvement in marks. However, for the subjective perception of learning achievement, pupils’ expectations was a significant predictor. These findings provide support for the use of pedagogical CAs in teaching but also highlight that the drivers of better learning outcomes still remain unknown