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In-depth analysis of customer journeys to broaden the understanding of customer behaviors and expectations in order to improve the customer experience is considered highly relevant in modern business practices. Recent studies predominantly focus on retrospective analysis of customer data, whereas more forward-directed concepts, namely predictions, are rarely addressed. Additionally, the integration of robotic process automation (RPA) to potentially increase the efficiency of customer journey analysis is not discussed in the current field of research. To fill this research gap, this paper introduces “customer journey mining”. Process mining techniques are applied to leverage digital customer data for accurate prediction of customer movements through individual journeys, creating valuable insights for improving the customer experience. Striving for improved efficiency, the potential interplay of RPA and customer journey mining is examined accordingly. The research methodology followed is based on a design science research process. An initially defined customer journey mining artifact is operationalized through an illustrative case study. This operationalization is achieved by analyzing a log file of an online travel agency functioning as an orientation for researchers and practitioners while also evaluating the initially defined framework. The data is used to train seven distinct prediction models to forecast the touchpoint a customer is most likely to visit next. Gradient-boosted trees yield the highest prediction accuracy with 43.1%. The findings further indicate technical suitability for RPA implementation, while financial viability is unlikely.
Virtual reality (VR) is starting to realize some of its promise as a tool to improve training effectiveness. However, research on VR for training and development is limited. Existing theories and models relating to organizational training and learning are infrequently used in the VR literature. A greater understanding of why VR works in the training context would help training designers create effective programs that leverage this continuously developing technology. This paper provides a typology of VR technologies specifically relevant to HR and integrates HR training frameworks and theory into findings on VR training from these other literatures. We specifically focus on immersive VR technology and seek to better understand reasons for the effectiveness of VR technologies for both training and assessment. We review findings, integrate related streams of research, and offer guideposts for those contemplating VR implementation in four important areas: training reactions in a VR context, VR-specific learning outcomes, opportunities for assessment using VR, and the effect of VR on training transfer. We conclude the paper by identifying a VR-training agenda for HR researchers.
This paper focusses on effective teaching and learning methods in the context of a larger project that aims to align objectives in higher education with employer requirements in the field of purchasing and supply management (PSM). The reason is that little is known about which specific skills and competencies of PSM professionals are needed outside academia and which learning objective higher education should incorporate to meet the practical PSM requirements of firms and organisations. Practice as well as literature share the understanding that PSM professionals need a well-balanced mixture of knowledge and soft-skills: the merely explicit know-what (codified knowledge), know-why (theory), know-how (method) and inter- & intrapersonal soft skills.
External sources of knowledge have become a necessary extension to internal innovation activities (Monteiro, Mol and Birkinshaw, 2017; Rosenkopf and Nerkar, 2001). Collaborations with customers, suppliers, universities or even competitors are a promising way to extend the own knowledge base in order to increase the firm´s innovativeness (Felin and Zenger, 2014; Laursen and Salter, 2006). onsidering this potential set of external partners, suppliers seem to have the largest impact on product innovation (Un, Cuervo-Cazurra and Asakawa, 2010). Yet, suppliers’ innovative potential is limited as described in a case study by Gassmann, Zeschky, Wolff, and Stahl (2010), which further shows how a new venture supplier, commonly referred to as “startup”, has succeed at providing a truly innovative idea (a haptic feedback control device for automobiles). Therefore, startups as a specific knowledge provider have received growing attention (Weiblen and Chesbrough, 2015; Zaremba, Bode and Wagner, 2016). By collaborating with startups, corporations hope to benefit from the startups´ entrepreneurial characteristics, such as alertness, creativity, flexibility and willingness to take risks (Audretsch, Segarra and Teruel, 2014; Criscuolo, Nicolaou and Salter, 2012; Marion, Friar and Simpson, 2012).
Startups have the potential to transform industries as they follow partly divergent business strategies and have the ability to develop new innovative products. The evolving fields of digitalization, sustainability and urbanization highlight the direction of change. Due to enormous time pressure and lack of knowledge, corporations rely heavily on external sources of knowledge to increase innovativeness. Therein, startups take a special role. Joint R&D projects, investments or strategic buyer-supplier agreements with startups grant corporations access to their innovative technologies. This paper gives insights into the organization of search processes to identify innovative startups and highlights approaches to initiate collaborations. Therefore, a multiple-case study among automotive OEMs and suppliers was conducted. The research ends with organizational structures, an identification process, and various instruments developed for the identification of startup innovations. Furthermore, propositions are made for a successful collaboration between startups and established corporations, displaying the role of purchasing in startup management, the need to take fast decisions, secure technical support by experts within their organization and build strong relationships with partners within their supply chain and new partners, as for example venture capitalists.
Process-Driven Applications (PDA) require less coding, for their business logic is defined by a business process model which can be executed by a process engine. However, inconsistencies between process model and dependent source code artifacts cause runtime errors and reduce development productivity. This paper targets at making the development of PDAs more efficient: It proposes a broader approach to statical analysis which also covers consistency constraints between model and code. When integrated into common analysis tools or a continuous integration pipeline, defects like broken code references or data-flow anomalies can be detected at an early stage without launching the entire application and its process interpretation engine. The approach is demonstrated by a prototype called viadee Process Application Validator (vPAV), which was developed for BPMN-based process models. The prototype has already been used in various BPM projects, attesting high benefit and potential.
In the context of Continuous Software Engineering, it is acknowledged as best practice to develop new features on the mainline rather than on separate feature branches. Unfinished work is then usually prevented from going live by some kind of feature toggle. However, there is no concept of feature toggles for Process-Driven Applications (PDA) so far. PDAs are hybrid systems consisting not only of classical source code but also of a machine-interpretable business process model. This paper elaborates on a feature development approach that covers both the business process model and the accompanying source code artifacts of a PDA. The proposed solution, Toggles for Process-Driven Applications (T4PDA), equipped with an easy to use modeling tool extension, enables the developer to safely commit unfinished work on model and source code to the project’s mainline. It will be kept inactive during productive deployments unless the feature is finally released. During an AB/BA crossover design experiment, the T4PDA approach, including the provided tool support, showed higher software quality, a faster development process, and contented developers.
Automated regression tests are a key enabler for applying popular continuous software engineering techniques. This paper focuses on testing BPMN-based Process-Driven Applications (PDA). When evolving PDAs, the affected test cases must be identified and co-evolved as well. In this process, affected test cases can be overlooked, misunderstandings may occur during communication between different roles involved, and implementation errors can arise. Regardless of possible error sources, the entire test migration process is time-consuming. This paper presents a new semi-automated test migration process for PDAs. The concept builds on previous work on creating regression tests using a no-code approach. Our approach identifies the modifications of the PDA and classifies their impact on previously defined tests. The classification indicates whether existing test code can be migrated automatically or whether a manual revision becomes necessary. During an AB/BA experiment, the concept and the developed prototype proved a more efficient test migration process and a higher test quality.
BPMN-based Process-Driven Applications (PDA) require less coding since they are not only based on source code, but also on executable process models. Automated testing of such model-driven applications gains growing relevance, and it becomes a key enabler if we want to found their development on continuous integration (CI) techniques.While process analysts are typically responsible for test case specifications from a business perspective, technically skilled process engineers take the responsibility for implementing the required test code. This is time-consuming and, due to their often different skills and backgrounds, might result in communication problems such as information losses and misunderstandings. This paper presents a new approach which enables an analyst to generate executable tests for PDAs without the need for manual coding. It consists of a sophisticated model analysis, a wizard-based specification of test cases, and a subsequent code generation. The resulting tests can easily be integrated into CI pipelines.The concept is underpinned by a user-friendly tool which has been evaluated in case studies and in real-world implementation projects from different industry sectors. During the evaluation, the prototype proved a more efficient test creation process and a higher test quality.
Process-Driven Applications flourish through the interaction between an executable BPMN process model, human tasks, and external software services. All these components operate on shared process data, so it is even more important to check the correct data flow. However, data flow is in most cases not explicitly defined but hidden in model elements, form declarations, and program code. This paper elaborates on data-flow anomalies acting as indicators for potential errors and how such anomalies can be uncovered despite implicit and hidden data-flow definitions. By considering an integrated view, it goes beyond other approaches which are restricted to separate data-flow analysis of either process model or source code. The main idea is to merge call graphs representing programmed services into a control-flow representation of the process model, to label the resulting graph with associated data operations, and to detect anomalies in that labeled graph using a dedicated data-flow analysis. The applicability of the solution is demonstrated by a prototype designed for the Camunda BPM platform.