Information Systems and e-Business Management最新文献

Changing business environments challenge and motivate organizations to transform. To remain competitive, organizations need to embrace these dynamics and make radical changes to how work is performed. Business process management (BPM) as a holistic management discipline offers mature methods and end-to-end management activities. However, it is subject to the tension between stability and change. While change through the improvement of existing business processes is well understood, the implementation and scaling of novel business processes have been neglected in BPM research. Hence, this paper proposes business process ramp-up management (BPRUM) as a new cross-cutting capability area for contemporary and future BPM and explores relevant sub-capabilities. Our work synthesizes insights from an exploratory interview study with 21 subject matter experts to advance the understanding of BPM as a corporate capability regarding the implementation and scaling of novel processes. As a result, this study illustrates how BPRUM adds to modern BPM and presents 40 action-oriented sub-capabilities that provide hands-on knowledge and practical guidance for effective BPRUM. Thereby, it serves as a foundation for further theorizing on process ramp-up and for structuring discussions among BPM practitioners.

Companies that frequently use robotic process automation often encounter difficulties in maintaining their RPA portfolio. To address these problems and reduce time spent investigating erratic behavior of RPA bots, developers can benefit from exploring methods from process sciences and applying them to RPA. After a selection process, we examine how variability and deviations impact robotic process automation. Indicators of statistical dispersion are chosen to assess variability and analyze RPA bot behavior. We evaluate the performance of RPA bots on 12 processes, using statistical dispersion as a measure. The results provide evidence that variability is an undesirable form of erratic behavior in RPA, as it strongly correlates with the success rate of the bots. Importantly, the results also show that outliers do not affect the success rate of RPA bots. This research suggests that variable analysis can help describe the behavior of RPA bots and assist developers in addressing erratic behavior. Additionally, by detecting variability, we can more effectively handle exceptions in RPA.

Prescriptive Analytics Systems (PAS) represent the most mature iteration of business analytics, significantly enhancing organizational decision-making. Recently, research has gained traction, with various technological innovations, including machine learning and artificial intelligence, significantly influencing the design of PAS. Although recent studies highlight these developments, the rising trend focuses on broader implications, such as the synergies and delegation between systems and users in organizational decision-making environments. Against this backdrop, we utilized a systematic literature review of 262 articles to build on this evolving perspective. Guided by general systems theory and socio-technical thinking, the concept of an information systems artifact directed this review. Our first objective was to clarify the essential subsystems, identifying 23 constituent components of PAS. Subsequently, we delved into the meta-level design of PAS, emphasizing the synergy and delegation between the human decision-maker and prescriptive analytics in supporting organizational decisions. From this exploration, four distinct system archetypes emerged: advisory, executive, adaptive, and self-governing PAS. Lastly, we engaged with affordance theory, illuminating the action potential of PAS. Our study advances the perspective on PAS, specifically from a broader socio-technical and information systems viewpoint, highlighting six distinct research directions, acting as a launchpad for future research in the domain.

RPA’s potential for efficiency improvement highly recommends the analysis of its applicability to the organizations’ processes. This paper analyses the value of RPA as its impact on the main activity groups in the value chain: Procurement, Internal Operations and Marketing and Sales. The analysis of RPA’s contribution for each of the activity groups addresses a research question through an exploratory approach: “How does RPA create value for organizations and which RPA features are critical for such value creation?”. The data was collected through interviews, analyzed using content analysis, and shows that RPA has impacts on both downstream and internal dimensions. Regarding upstream dimension further research is required to ascertain RPA’s potential.

Business process management (BPM) aims to help organizations manage their business processes. Startups differ from established firms as they go through different phases of prospecting, developing, and exploiting the new venture. Startups begin to focus on the organization of their processes after they reach the exploiting (scale-up) phase. Digital startups are unique as information technology (IT) becomes the business model itself. These unique characteristics raise a question: how do digital startups at the scale-up phase manage their business processes? To answer the question, two case studies on digital startups in logistics providers are conducted. The case studies are designed to be inductive in nature. Grounded Theory Method (GTM) is used for data collection and analysis. Data is collected via interviews and supporting documents. The BPM capability provides the basis to create guiding questions for the interviews. The interview results are analyzed with a grounded theory approach of open, theoretical, and selective coding. To derive a new theory, cross-case analyses are conducted. Findings from two digital startups allow us to identify important categories that play a role in how digital startups manage their activities: industry and stakeholders, digital offerings, organic structure, process management, performance measurement, employee training and culture. We further theorize that the competitive nature of startups makes them customer-centric and focus on agility. Digital startups continuously improve their product and conduct adaptive process experimentation involving a cycle of process identification, IT-based process implementation and process adaptation. The supporting capabilities that enable the process management of digital startups are agile people and culture and organic structure.

Business process redesign has received considerable attention in research and practice. An important task contributing to redesign is idea generation. It has been argued that the choice of technique influences redesign outcomes, but empirical evidence is largely missing. In this paper, we examine the role of idea generation in business process redesign by comparing two different kinds of techniques: exploitative and explorative redesign techniques. Often, exploitative techniques are expected to yield more appropriate solutions, while explorative techniques are associated with more creative outcomes. To investigate such propositions, we consider the Best Practices of Process Redesign as an example of an exploitative redesign technique and the Business Process Design Space as an explorative redesign technique. We conducted a free-simulation experiment to study the empirical impact of the choice of redesign techniques on outcomes. We find that the Business Process Design Space leads to a higher number of redesign ideas, which are also more diverse. These are more creative and novel than ideas produced by the Best Practices of Process Redesign. Against expectation, the best practices as an exploitative approach do not produce more appropriate nor impactful ideas in our task design. Since both approaches cover different areas of improvement, a key implication of our work is that they should not be seen as isolated approaches to redesign; rather, they should be purposefully combined to not only create novel but also operational business process redesigns.

Driven by technological progress, business analytics is gaining momentum while paving the path for next-generation business process management. Especially, embedded real-time analytics offers new opportunities for business process intelligence and value creation. However, there are several obstacles that organizations face in their adoption process. A key challenge is to identify business processes that are suitable for embedded analytics and hold relevant value potential. Our research addresses this need by introducing an exploratory BPM method, namely a process selection method. Applying action design research and situational method engineering, we iteratively built, used, evaluated, and refined the theory-ingrained method artifact. The method provides organizations with guidance in selecting operational business processes, for which a reengineering project should be initiated.

The core success of digital transformation among small and medium enterprises (SMEs) still needs to be clarified and comprehensively discussed. Meanwhile, SMEs cannot exploit digital advantages due to their limited strategic empirical and practical knowledge. This study aims to (1) describe the research pattern in the SME’s digitalization area; (2) identify the core success factors of SMEs' digital transformation to gain business success; (3) search for relevant business aspects that are necessary for SME digital transformation; and (4) develop further research agenda in the field of SMEs’ digital transformation. This study uses a systematic literature review and thematic analysis to determine core success factors suggested by primary studies. The literature review suggests that (1) SMEs should consider their baseline, limitation, and idiosyncratic to develop an aligned digitalization strategy; (2) SMEs should consider starting with incremental and gradually improving digitalization; and (3) SMEs should commit to investing in education and continuous learning.

Past generations of BPM involved the efficient and effective management of business processes. Yet, we currently face a turning point. The technological facets of data-driven BPM add complexity to traditional BPM applications. As a result, organizations face intended and unintended technology-related changes across all business process initiatives. Using the term BP-x, we summarize the recent changes in BPM knowledge that has hitherto been fragmented across academic literature. To address the challenges of BP-x initiatives, we envision a holistic model that focuses on managing related cutting-edge technologies and BPM. Thus, we propose the operationalized BP-x management model as a valuable IT meta-artifact. We develop the model using a two-cycled Design Science Research methodology and conduct a threefold summative evaluation. The results of our study indicate that creating awareness of potentials and opportunities accelerates the process toward action and fosters new business outcomes in terms of performance and innovation. By strategically aligning BP-x endeavors, organizations promote visibility, shared understanding, and culture among stakeholders. Our model guides managers throughout the BP-x adoption journey in conjunction with organizational, managerial, and technological prerequisites.

Many enterprises are currently engaged in developing blockchain-based business models. Enterprise networks offer a variety of potential applications for blockchain solutions as they benefit from transparency and security as well as automation of handling data, material, and financial flows along their supply chains. Despite profound potentials, the indicated business models are still in their early stages and need further investigation. To provide an overview of existing blockchain-based business models in the context of enterprise networks, the underlying paper designs a multidimensional taxonomy and identifies several archetypes of blockchain-based businesses. For the taxonomy development, data from 101 blockchain start-ups serves as a basis for empirical validation. Using hierarchical clustering and the k-means method, seven archetypes that sharpen the understanding of how blockchain solutions affect business models in enterprise networks and enable new business models are derived. The proposed work results are intended to be applied in future research and practice to classify and assess the integration of blockchain solutions into existing business models and to support developing new ones that leverage emerging technological capabilities.