Jan C. Recker

Bio: Jan C. Recker is an academic researcher from University of Cologne. The author has contributed to research in topics: Process modeling & Business process management. The author has an hindex of 52, co-authored 348 publications receiving 11233 citations. Previous affiliations of Jan C. Recker include Queensland University of Technology & University of Hamburg.

Blockchains for Business Process Management - Challenges and Opportunities

Abstract: Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.

Business process modeling - a comparative analysis

Abstract: Many business process modeling techniques have been proposed over the last decades, creating a demand for theory to assist in the comparison and evaluation of these techniques. A widely established way of determining the effectiveness and efficiency of modeling techniques is by way of representational analysis. This paper comparatively assesses representational analyses of 12 popular process modeling techniques in order to provide insights into the extent to which they differ from each other. We discuss several implications of our findings. Our analysis uncovers and explores representational root causes for a number of shortcomings that remain in process modeling practice, such as lack of process decomposition and integration of business rule specification. Our findings also serve as motivation and input to future research in areas such as context-aware business process design and conventions management.

Sensemaking and sustainable practicing: functional affordances of information systems in green transformations

Abstract: This paper explores how a world-wide operating software solutions provider implemented environmentally sustainable business practices in response to emerging environmental concerns Through an interpretive case study, we develop a theoretical framework that identifies four important functional affordances originating in information systems, which are required in environmental sustainability transformations as they create an actionable context in which (1) organizations can engage in a sensemaking process related to understanding emerging environmental requirements, and (2) individuals can implement environmentally sustainable work practices Through our work, we provide several contributions, including a better understanding of IS-enabled organizational change and the types of functional affordances of information systems that are required in sustainability transformations We describe implications relating to (1) how information systems can contribute to the creation of environmentally sustainable organizations, (2) the design of information systems to create required functional affordances, (3) the management of sustainability transformations, and (4) the further development of the concept of functional affordances in IS research

How Much Language Is Enough? Theoretical and Practical Use of the Business Process Modeling Notation

Abstract: The Business Process Modeling Notation (BPMN) is an increasingly important industry standard for the graphical representation of business processes. BPMN offers a wide range of modeling constructs, significantly more than other popular languages. However, not all of these constructs are equally important in practice as business analysts frequently use arbitrary subsets of BPMN. In this paper we investigate what these subsets are, and how they differ between academic, consulting, and general use of the language. We analyzed 120 BPMN diagrams using mathematical and statistical techniques. Our findings indicate that BPMN is used in groups of several, well-defined construct clusters, but less than 20% of its vocabulary is regularly used and some constructs did not occur in any of the models we analyzed. While the average model contains just 9 different BPMN constructs, models of this complexity have typically just 4-5 constructs in common, which means that only a small agreed subset of BPMN has emerged. Our findings have implications for the entire ecosystems of analysts and modelers in that they provide guidance on how to reduce language complexity, which should increase the ease and speed of process modeling.

How Much Language is Enough? Theoretical and Practical Use of the Business Process Modeling Notation

Abstract: The Business Process Modeling Notation (BPMN) is an increasingly important industry standard for the graphical representation of business processes. BPMN offers a wide range of modeling constructs, significantly more than other popular languages. However, not all of these constructs are equally important in practice as business analysts frequently use arbitrary subsets of BPMN. In this paper we investigate what these subsets are, and how they differ between academic, consulting, and general use of the language. We analyzed 120 BPMN diagrams using mathematical and statistical techniques. Our findings indicate that BPMN is used in groups of several, well-defined construct clusters, but less than 20% of its vocabulary is regularly used and some constructs did not occur in any of the models we analyzed. While the average model contains just 9 different BPMN constructs, models of this complexity have typically just 4-5 constructs in common, which means that only a small agreed subset of BPMN has emerged. Our findings have implications for the entire ecosystems of analysts and modelers in that they provide guidance on how to reduce language complexity, which should increase the ease and speed of process modeling.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

기독교 사역과 Leadership

Abstract: Building Leadership Effectiveness This program encourages leaders to develop practices that transform values into action, vision into realities, obstacles into innovations, and risks into rewards. Participants will be introduced to the five practices of exemplary leadership: modeling the way, inspiring a shared vision, challenging the process, enabling others to act, and encouraging the heart Coaching & Communicating for Performance Coaching & Communicating for Performance is a highly interactive program that will give supervisors and managers the opportunity to build skills that will enable them to share expectations and set objectives for employees, provide constructive feedback, more effectively engage in learning conversations, and coaching opportunities. Skillful Conflict Management for Leaders As a leader, it is important to understand conflict and be effective at conflict management because the way conflict is resolved becomes an integral component of our university’s culture. This series of conflict management sessions help leaders learn and put into practice effective strategies for managing conflict.

Constructing Grounded Theory: A Practical Guide through Qualitative Analysis

Abstract: การวจยเชงคณภาพ เปนเครองมอสำคญอยางหนงสำหรบทำความเขาใจสงคมและพฤตกรรมมนษย การวจยแบบการสรางทฤษฎจากขอมล กเปนหนงในหลายระเบยบวธการวจยเชงคณภาพทกำลงไดรบความสนใจ และเปนทนยมเพมสงขนเรอยๆ จากนกวชาการ และนกวจยในสาขาสงคมศาสตร และศาสตรอนๆ เชน พฤตกรรมศาสตร สงคมวทยา สาธารณสขศาสตร พยาบาลศาสตร จตวทยาสงคม ศกษาศาสตร รฐศาสตร และสารสนเทศศกษา ดงนน หนงสอเรอง “ConstructingGrounded Theory: A Practical Guide through Qualitative Analysis” หรอ “การสรางทฤษฎจากขอมล:แนวทางการปฏบตผานการวเคราะหเชงคณภาพ” จะชวยใหผอานมความรความเขาใจถงพฒนาการของปฏบตการวจยแบบสรางทฤษฎจากขอมล ตลอดจนแนวทาง และกระบวนการปฏบตการวจยอยางเปนระบบ จงเปนหนงสอทควรคาแกการอานโดยเฉพาะนกวจยรนใหม เพอเปนแนวทางในการนำความรความเขาใจไประยกตในงานวจยของตน อกทงนกวจยผเชยวชาญสามารถอานเพอขยายมโนทศนดานวจยใหกวางขวางขน