Willem Mertens

Ten principles of good business process management

Purpose – The purpose of this paper is to foster a common understanding of business process management (BPM) by proposing a set of ten principles that characterize BPM as a research domain and guide its successful use in organizational practice. Design/methodology/approach – The identification and discussion of the principles reflects the viewpoint, which was informed by extant literature and focus groups, including 20 BPM experts from academia and practice. Findings – The authors identify ten principles which represent a set of capabilities essential for mastering contemporary and future challenges in BPM. Their antonyms signify potential roadblocks and bad practices in BPM. The authors also identify a set of open research questions that can guide future BPM research. Research limitations/implications – The findings suggest several areas of research regarding each of the identified principles of good BPM. Also, the principles themselves should be systematically and empirically examined in future studies....

Practices of knowledge intensive process management: quantitative insights

Purpose – In contemporary businesses, the importance of knowledge workers and the knowledge intensive business processes (KIBPs) is ever increasing, yet they seem very hard to control and manage. The purpose of this paper is to grasp the specific characteristics of KIBPs and how they differ from non‐knowledge intensive business processes (non‐KIBP), also to question how organizations are using business process management (BPM) to manage and improve KIBPs. The differences in maturity and effectiveness of both types of processes are also evaluated.Design/methodology/approach – Data for this research were collected through an online survey. The survey was designed based on a previously conducted exploratory study with semi‐structured interviews as well as the literature. The target group was BPM practitioners and the final sample included 98 respondents. Due to non‐normality, the analyses were conducted with non‐parametric tests. The research questions were analysed using Mann‐Whitney U test and Spearmans c...

From business process management to customer process management

Purpose – The purpose of this paper is to argue that in order to achieve customer centricity through business process management (BPM), companies have to obtain the profound understanding of customers’ processes and when necessary change not only the interactions with but also the processes of their customers. A method is presented that allows doing this in a systematic manner. Design/methodology/approach – A case study of a large multinational company was conducted. Several different sources and methods were used, including document analysis, interviews and a qualitative analysis of responses to open-ended questions. Data were gathered at three points in time: before, during and after the implementation of the presented approach. Findings – The method that was successfully employed by the case organisation consisted of combining BPM with service blueprinting, and of extending these efforts by integrating the customers’ internal processes into the scope of improvement. Research limitations/implications – ...

A Framework for the Study of Positive Deviance in Organizations

ABSTRACT Positive deviance describes behavior that deviates from salient norms, yet is positive in its intention or effect. It has received considerable attention in the sociology literature, and is increasingly being studied in the context of the workplace. The development of a cumulative tradition in this domain, however, is hindered by the continued coexistence of multiple definitions of positive deviance in organizations and the use of various measurements and research designs. In this article, we synthesize existing definitions and approaches to the organizational study of positive deviance, integrate them into a coherent conceptual framework, and offer methodological advice and illustrations.

Complete and Interpretable Conformance Checking of Business Processes

This article presents a method for checking the conformance between an event log capturing the actual execution of a business process, and a model capturing its expected or normative execution. Given a process model and an event log, the method returns a set of statements in natural language describing the behavior allowed by the model but not observed in the log and vice versa. The method relies on a unified representation of process models and event logs based on a well-known model of concurrency, namely event structures. Specifically, the problem of conformance checking is approached by converting the event log into an event structure, converting the process model into another event structure, and aligning the two event structures via an error-correcting synchronized product. Each difference detected in the synchronized product is then verbalized as a natural language statement. An empirical evaluation shows that the proposed method can handle real datasets and produces more concise and higher-level difference descriptions than state-of-the-art conformance checking methods. In a survey designed according to the technology acceptance model, practitioners showed a preference towards the proposed method with respect to a state-of-the-art baseline.

Knowledge Intensive Business Processes: An Exploratory Study

In todays business world, the role of knowledge and knowledge intensive business processes (KIBPs) is ever increasing. Subsequently the capability of Business Process Management (BPM) in managing KIBPs is being questioned. Academics and practitioners alike suggest that BPM needs to evolve into a more flexible discipline that is capable of dealing with KIBPs. As a first step towards this goal, it is important to understand KIBPs better and the way they differ from non-KIBPs. Based on 20 in-depth interviews with process owners from 10 different organizations, this exploratory work provides insight into the identification of KIBPs and how they differ from non-KIBPs in terms of characteristics. The results confirm most of the suggestions in theory, yet challenges some of the existing categorizations such as the continuum of structured to unstructured processes. Furthermore, a range of information sources used to manage both types of processes are identified, suggesting new areas for research.

Positive Deviance and Leadership: An Exploratory Field Study

Positive deviance refers to behavior that deviates from the norms of the reference group and has positive effects on the organization. It is an endogenous source of organizational creativity that has been shown to be powerful tool for learning and change. Despite growing interest, little remains known about the factors that stimulate positive deviance; in particular, how management can enable its emergence. In this paper, we explore the relationship between leadership and positive deviance through a conversion mixed methods field study of two hierarchical layers of store management in a large Australian retailer. Our findings indicate that management can best enable the emergence of positive deviance by combining empowering leadership behaviors with adequate levels of contingent reward and monitoring behaviors. These findings suggest that, depending on the frame of reference, positive deviance may emerge as a source for innovation that is endogenous to routines, rather than deviance from routines.

How to Start Analyzing, Test Assumptions and Deal with that Pesky p-Value

This chapter discusses the steps to take before any of the analyses discussed in earlier chapters. Although it may seem counterintuitive to put this information in the last chapter, experience teaches us that these are things people do not want to read first when they embark on their analysis journey. We all start out with a big idea and full of courage, but all too often our courage is blown to bits because words and terms like “homoscedasticity,” “skewness,” and “multivariate normality” make our heads spin and our plans seem impossible. However, we hope that, after you have gotten a kick from seeing first results with the method of your choice, you are now ready to learn about all the things you should have done first—the things that make your results credible.

Models with Latent Concepts and Multiple Relationships: Structural Equation Modeling

One of the best-known models in Information Systems research is the Technology Acceptance Model (TAM), which postulates that users will intend to use a system if they find it useful and easy to use, and that they will find a system useful if they find it is easy to use. This model has been studied over and over again, typically by surveying users (or even non-users) of some system with questions about the degree to which they find the system useful and/or easy to use and whether they intend to use it in the future.

Nested Data and Multilevel Models: Hierarchical Linear Modeling

Most of the people and cases that are subject to research in business and information systems are nested within hierarchies. A hierarchy attaches roles to certain levels and typically makes higher-level roles responsible for lower-level roles. At all levels of the organizational hierarchy, this approach translates into small clusters of managers and larger clusters of team members (which may include managers of lower-level teams). Such a hierarchy could range from a CEO and her team of executives to a line manager and his team of operators, but the hierarchy even continues beyond the organization, as organizations are nested within industries, industries within countries, and so on. Sometimes we want to study effects that cross these hierarchical layers. For example, we may be interested in the effect of managers’ behavior on their team members’ behavior, or the effect of remuneration policies at the level of the organization on individual performance and individual turnover intentions. In other words, we may want to study the effect of a variable that varies at the group level (i.e., between groups) on another variable that differs for every individual (i.e., it varies within groups). This kind of investigation calls for the use of hierarchical linear models.