Digital transformation is highly relevant to most organisations in the business and the government sectors. One important aspect of digital transformation is the capability to exploit data in order to develop new services. For a number of businesses, this capability has become an imperative to their survival in an ever more competitive market. Today, data exploitation is of vital importance for innovation and economic growth. However, there is a lack of consolidated knowledge about the challenges of managing processes for data-driven innovation. Therefore, the purpose of this study is to elaborate on challenges concerning data-driven innovation. We have used the Grounded Theory approach to identify such challenges which are: lack of a systematic process, problems with data access, distrust of data, lack of appropriate digital tools and insufficient competence. Our conclusions reveal that data is rarely used as a strategic resource in data-driven innovation and that there is a lack of data management.

Distance Education can have students in a course that are geographically and culturally distributed. This is also an issue resulting from common industrial practice of the use of outsourcing and corporations that are international in nature.

Consensus is an important measure for the success of any business process modeling effort. Although intensively studied in the general literature on group processes, consensus has hardly been considered in business process modeling and never seriously measured. The author defines consensus as the level of agreement of group members' views on the process and introduce business process similarity as a proxy. The author validates the measure by comparing it to an existing self-reported measure of consensus. The author then uses this measure to inform and guide the process of modeling.

We study the impact of end-user involvement and team factors on model quality and consensus. By end-user involvement we mean the degree to which participants of a modeling session are involved in the active creation of models, i.e. the drawing of the diagram. We find that higher end-user involvement, facilitated by tool support, increases model quality. Complementary teams achieve better consensus than matched teams.

This paper gives an overview of the recent field of collaborative modelling. We first address the dimensions of group productivity, model quality and consensus. We then explore the factors on individual and team level that have significant impact on group productivity. We continue with an overview of the most relevant streams of collaborative modelling research, and conclude with a short outline of the papers in this special issue.

In business processes, modeling is usually a collaborative activity. In it stakeholders analyze or design business processes. One of the challenges is that group members have diverse backgrounds and conflicting interests which make it difficult to arrive at a model that represents a consensus. It is therefore important to study the way in which modeling teams are organized to overcome these problems. To approach this issue we investigated the modeling behavior of such groups with the help of a tool that supports collaborative modeling while at the same time allowing for the effective collection of data on modeling activities. Besides confirming known roles we also discovered a new one, editor, that only emerges in tool-supported sessions and that functions as a mediator between modeling experts and domain experts.

Consensus is an important measure for the success of any business process modeling effort. Although intensively studied in the general literature on group processes, consensus has hardly been considered in business process modeling and never seriously measured. We define consensus as the level of agreement of group members’ views on the process and introduce business process similarity as a proxy. We validate the measure by comparing it to an existing self-reported measure of consensus.

The collaborative design of business and information systems touches a number of issues that lie within the realm of different research areas. It deals with design as such and in particular with design in and for groups. It is also concerned with socio-technical systems and hence with human-computer interaction as well as IT-mediated human-human interaction. This introduces collaboration issues. The significant complexity of the business and information systems that are in the focus of the design endeavor calls for modeling as an instrument for managing this complexity. This paper maps the terrain of collabora- tive design of business and information systems by surveying the contributions that are made by related areas of research.

Many studies have looked at the factors that control the productivity of collaborative work. We claim that goal commitment and competition have a strong impact on group productivity in collaborative modelling. To substantiate this claim we first take a look at existing factor models to identify the factors that potentially mediate the effect on group productivity. We then investigate the relation between the factors with the help of controlled field experiments in five different organisations. We confirm the theoretical results with the help of structured equation modelling.

Many studies have looked at the factors that control the productivity of collaborative work. We claim that goal commitment and competition have a strong impact on group productivity in collaborative modelling. To substantiate this claim we first take a look at existing factor models to identify the factors that potentially mediate the effect on group productivity. We then investigate the relation between the factors with the help of controlled field experiments in five different organisations. We confirm the theoretical results with the help of structured equation modelling.