How to improve the assessment of BPM maturity in the era of digital transformation

For almost 30 years, the way of building business process management maturity models (BPM MMs), the importance assigned to individual maturity levels, and the criteria and critical success factors chosen for BPM maturity assessment have not changed significantly, despite the fact that during those three decades, the business environment and organizations themselves have changed enormously. The impact of hyperautomation and the increasing pace of change require the integration of maturity assessment with the BPM implementation methodology, including the repetition of maturity assessment for selected groups of processes. This causes an urgent need to adapt both process maturity assessment methods and BPM MMs to changing working conditions and business requirements. This conceptual paper is based on a model approach. The framework presented in the article continues and at the same time clearly deviates from the tradition of building BPM MMs on the basis of the Capability Maturity Model (CMM). It proposes a two-stage comprehensive process of organizational process maturity assessment, fully integrated into the process of BPM implementation and further business process management. The presented framework makes it possible to assess the process maturity of Industry 4.0 organizations in which dynamic knowledge-intensive business processes (kiBPs) play a key role in creating value.

Success factors of long-term CIOs

Although the average tenure of CIOs has increased over the last years, the majority of CIOs have been in their positions for only three years or less. Nevertheless, some CIOs have been successful in their position for a long time. In this study, we use tenure as a proxy for success as a CIO. The goal of this paper is to examine factors that are critical to the success of long-term CIOs. For this purpose, we created and analyzed resumes of 384 CIOs. Out of these 384, we conducted 19 interviews with CIOs from top-tier companies and collected and analyzed both qualitative and quantitative data. In the process, we were able to identify nine factors that are critical for the success (CSF) of CIOs. These factors fall into three categories. Category “Personality” includes “Accepting and embracing change” (CSF #1), “Being perseverant to pursue long-term goals” (CSF #2), “Anticipating the future through visionary thinking” (CSF #3), and “Being empathetic to deal with uncertainty felt by co-workers” (CSF #4). The “Role Fulfilment” category includes “Cross-functional involvement and integration of the IT organization” (CSF #5), “Positioning and restructuring of the IT organization” (CSF #6), and “Well-connected and communicative leadership” (CSF #7). The “Organizational Environment” category consists of “Availability of skilled workforce” (CSF #8) and “Reporting line to the CEO” (CSF #9). CSFs 1, 2, and 3 were perceived as most important by the participating CIOs. The results may be of particular interest both to aspiring CIOs and equally their employing organizations, as they reflect what long-term CIOs value during their time in office.

UR: SMART–A tool for analyzing social media content

The digital transformation, with its ongoing trend towards electronic business, confronts companies with increasingly growing amounts of data which have to be processed, stored and analyzed. Instant access to the “right” information at the time it is needed is crucial and thus, the use of techniques for the handling of big amounts of unstructured data, in particular, becomes a competitive advantage. In this context, one important field of application is digital marketing, because sophisticated data analysis allows companies to gain deeper insights into customer needs and behavior based on their reviews, complaints as well as posts in online forums or social networks. However, existing tools for the automated analysis of social content often focus on one general approach by either prioritizing the analysis of the posts’ semantics or the analysis of pure numbers (e.g., sum of likes or shares). Hence, this design science research project develops the software tool UR:SMART, which supports the analysis of social media data by combining different kinds of analysis methods. This allows deep insights into users’ needs and opinions and therefore prepares the ground for the further interpretation of the voice. The applicability of UR:SMART is demonstrated at a German financial institution. Furthermore, the usability is evaluated with the help of a SUMI (Software Usability Measurement Inventory) study, which shows the tool’s usefulness to support social media analyses from the users’ perspective.