1,721,301 research outputs found
From business process management to customer process management
No full textPurpose – 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 – The paper does not thoroughly evaluate the long-term effects of the proposed approach. Some results of the case study analysis had to be excluded from this paper due to reasons of confidentiality.
Practical implications – The paper presents an approach for organisations to not only understand the needs of their customers but also the way in which their product is used in customers’ processes. In this way BPM can be implemented in a truly customer-oriented way.
Originality/value – This paper extends previous work by presenting one way in which BPM can follow up on its promise of increasing an organisations customer orientation. While servitisation has received a lot of attention in various disciplines, its application within BPM research and practice has been scarce
Positive deviance: a study of measurements and determinants.
No full textThe objective of this dissertation is to provide theoretical, methodological and empirical advances to research on positive deviance in organisations. Positive deviance refers to behaviour or the outcome of behaviour that positively deviates from salient norms of a reference group without negatively affecting other groups. This reference group can be the team, department, division, or the organisation as a whole. In other words, positive deviance describes unusual or unexpected improvements that have been spontaneously developed by members of the organisation. Harvesting these positive variations has countless advantages in terms of resource use, change dynamics and organisational learning.Despite the growing number of accounts that testify to the potential of positive deviance for organisational learning and change, the body of research is incomplete in a number of ways. First, a clear consensus on the definition and boundaries of positive deviance has not been reached. Second, strong empirical studies are lacking; most studies to date rely on a single method and demonstrate little or no construct validity and reliability. Third, little guidance exists on how to do it better. In my PhD, I aim to alleviate these conceptual and empirical challenges and set out to answer three main research questions:RQ1: How to define, operationalize and study positive deviance in organisations?RQ2: What are the individual determinants of positive deviance?RQ3: How can management enable the emergence of positive deviance?I aim to provide answers to these research questions in three research papers. The first paper presents a structured overview of existing definitions of positive deviance and describes their differences. Next, it describes a framework for the empirical study of positive deviance in organisations that can be applied to any chosen definition. The second and third paper report on two empirical studies that apply this framework in a large Australian retail organisation. Through a mixed methods field study described in Paper II, I first explore and test a number of individual determinants of positive deviance in highly standardised operational processes of in-store bakery departments. In this paper, positive deviance is operationalized in a context-specific way and the findings demonstrate that is a discriminable and in other ways valid concept that can positively contribute to the organisational performance. Further, the results suggest that employees that (1) work in a highly standardised context, (2) have a desire to do their job well, (3) feel empowered by their job and context, and (4) possess the skills necessary to do their job well will more likely engage in positive deviance. In a next step I explore whether leaders can enable the emergence of positive deviance at and across the multiple levels of store management. In the study presented in Paper III, positive deviance is measured using a previously validated self-report scale rather than the context-specific way used in Paper II. The findings show thatmeasured this waypositive deviance is not significantly associated with performance. Further, contradicting the general assumption and the findings of Paper II that empowered employees will engage more in positive deviance, the results of Paper III suggest that empowered employees deviate less. Because of these contradictory findings, I next report on auxiliary analyses that compare the data of Paper II and Paper III. These analyses shed light on why different ways of measuring positive deviance return different results, and further sharpen its conceptual boundaries. In conclusion, it appears that positive deviance occurs, that it contributes to the organisational performance, that it is more frequently engaged in by skilled and empowered employees, but that these empowered employees do not consider their behaviour to be deviant.status: Publishe
Essays over de Toekomst van Mobiliteit van Werknemers
No full textIn this dissertation, we empirically examine a new model of employee mobility in large, hierarchical organizations. Our focus is on three guiding questions: (1) What processes are replacing conventional mobilization and development approaches? And what technologies are expected to play a key role therein?; (2) What technology design are organizations applying to revise their approaches to employee mobility and development?; (3) How do employees experience individual work and development in these new marketplaces of work? We find that traditional mobility models are being replaced by more nimble and market-like forms of mobility that provide employees unprecedented opportunities to self-direct their careers within their broader organization. We emphasize both the theoretical and practical implications of our findings and identify several areas of future inquiry.status: Publishe
Transforming government: the way towards digital era governance.
No full textThe aim of this research chair is to conduct scientific research on the possibilities for the digitization of public services. This includes research into innovation of business processes, services and service models within digital ecosystems for public services.status: Publishe
A comparison of state-of-the-art classification techniques for expert automobile insurance fraud detection
No full textSeveral state–of–the–art binary classification techniques are experimentally evaluated in the context of expert automobile insurance claim fraud detection. The predictive power of logistic regression, C4.5 decision tree, k–nearest neighbor, Bayesian learning multilayer perceptron neural network, least–squares support vector machine, naive Bayes, and tree–augmented naive Bayes classification is contrasted. For most of these algorithm types, we report on several operationalizations using alternative hyperparameter or design choices. We compare these in terms of mean percentage correctly classified (PCC) and mean area under the receiver operating characteristic (AUROC) curve using a stratified, blocked, ten–fold cross–validation experiment. We also contrast algorithm type performance visually by means of the convex hull of the receiver operating characteristic (ROC) curves associated with the alternative operationalizations per algorithm type. The study is based on a data set of 1,399 personal injury protection claims from 1993 accidents collected by the Automobile Insurers Bureau of Massachusetts. To stay as close to real–life operating conditions as possible, we consider only predictors that are known relatively early in the life of a claim. Furthermore, based on the qualification of each available claim by both a verbal expert assessment of suspicion of fraud and a ten–point–scale expert suspicion score, we can compare classification for different target/class encoding schemes. Finally, we also investigate the added value of systematically collecting nonflag predictors for suspicion of fraud modeling purposes. From the observed results, we may state that: (1) independent of the target encoding scheme and the algorithm type, the inclusion of nonflag predictors allows us to significantly boost predictive performance; (2) for all the evaluated scenarios, the performance difference in terms of mean PCC and mean AUROC between many algorithm type operationalizations turns out to be rather small; visual comparison of the algorithm type ROC curve convex hulls also shows limited difference in performance over the range of operating conditions; (3) relatively simple and efficient techniques such as linear logistic regression and linear kernel least–squares support vector machine classification show excellent overall predictive capabilities, and (smoothed) naive Bayes also performs well; and (4) the C4.5 decision tree operationalization results are rather disappointing; none of the tree operationalizations are capable of attaining mean AUROC performance in line with the best. Visual inspection of the evaluated scenarios reveals that the C4.5 algorithm type ROC curve convex hull is often dominated in large part by most of the other algorithm type hulls.<br/
Bayesian neural network learning for repeat purchase modelling in direct marketing
Full text linkWe focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted. The evidence framework of MacKay is used as an example implementation of Bayesian neural network learning, a method that is fairly robust with respect to problems typically encountered when implementing neural networks. The automatic relevance determination (ARD) method, an integrated feature of this framework, allows us to assess the relative importance of the inputs. The basic response models use operationalisations of the traditionally discussed Recency, Frequency and Monetary (RFM) predictor categories. In a second experiment, the RFM response framework is enriched by the inclusion of other (non-RFM) customer profiling predictors. We contribute to the literature by providing experimental evidence that: (1) Bayesian neural networks offer a viable alternative for purchase incidence modelling; (2) a combined use of all three RFM predictor categories is advocated by the ARD method; (3) the inclusion of non-RFM variables allows to significantly augment the predictive power of the constructed RFM classifiers; (4) this rise is mainly attributed to the inclusion of customer/company interaction variables and a variable measuring whether a customer uses the credit facilities of the direct mailing company
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
