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Assessing business process complexity based on textual data: Evidence from ITIL IT ticket processing
(2021)
Towards a Business Process Complexity Analysis Framework Based on Textual Data and Event Logs
(2022)
An approach for analyzing business process execution complexity based on textual data and event log
(2023)
With the advent of digital transformation, organizations increasingly rely on various information systems to support their business processes (BPs). Recorded data, including textual data and event log, expand exponentially, complicating decision-making and posing new challenges for BP complexity analysis in Business Process Management (BPM). Herein, Process Mining (PM) serves to derive insights based on historic BP execution data, called event log. However, in PM, textual data is often neglected or limited to BP descriptions. Therefore, in this study, we propose a novel approach for analyzing BP execution complexity by combining textual data serving as an input at the BP start and event log. The approach is aimed at studying the connection between complexities obtained from these two data types. For textual data-based complexity, the approach employs a set of linguistic features. In our previous work, we have explored the design of linguistic features favorable for BP execution complexity prediction. Accordingly, we adapt and incorporate them into the proposed approach. Using these features, various machine learning techniques are applied to predict textual data-based complexity. Moreover, in this prediction, we show the adequacy of our linguistic features, which outperformed the linguistic features of a widely-used text analysis technique. To calculate event log-based complexity, the event log and relevant complexity metrics are used. Afterward, a correlation analysis of two complexities and an analysis of the significant differences in correlations are performed. The results serve to derive recommendations and insights for BP improvement. We apply the approach in the IT ticket handling process of the IT department of an academic institution. Our findings show that the suggested approach enables a comprehensive identification of BP redesign and improvement opportunities.
The public sector faces several challenges, such as a number of external and internal demands for change, citizens' dissatisfaction and frustration with public sector organizations, that need to be addressed. An alternative to the traditional top-down development of public services is co-creation of public services. Co-creation promotes collaboration between stakeholders with the aim to create better public services and achieve public values. At the same time, data analytics has been fuelled by the availability of immense amounts of textual data. Whilst both co-creation and TA have been used in the private sector, we study existing works on the application of Text Analytics (TA) techniques on text data to support public service co-creation. We systematically review 75 of the 979 papers that focus directly or indirectly on the application of TA in the context of public service development. In our review, we analyze the TA techniques, the public service they support, public value outcomes, and the co-creation phase they are used in. Our findings indicate that the TA implementation for co-creation is still in its early stages and thus still limited. Our research framework promotes the concept and stimulates the strengthening of the role of Text Analytics techniques to support public sector organisations and their use of co-creation process. From policy-makers' and public administration managers' standpoints, our findings and the proposed research framework can be used as a guideline in developing a strategy for the designing co-created and user-centred public services.
Context awareness is critical for the successful execution of processes. In the abundance of business process management (BPM) research, frameworks exclusively devoted to extracting context from textual process data are scarce. With the deluge of textual data and its increasing value for organizations, it becomes essential to employ relevant text analytics techniques to increase the awareness of process workers, which is important for process execution. The present paper addresses this demand by developing a framework for context awareness based on process executions-related textual data using a well-established layered BPM context model. This framework combines and maps various text analytics techniques to the layers of the context model, aiming to increase the context awareness of process workers and facilitate informed decision-making. The framework is applied in an IT ticket processing case study. The findings show that contextual information obtained using our framework enriches the awareness of process workers regarding the process instance urgency, complexity, and upcoming tasks and assists in making decisions in terms of these aspects.