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Journal ArticleDOI

Communication flow orientation in business process modeling and its effect on redesign success: Results from a field study

TLDR
A focus on communication flows in business processes is an important ingredient in successful business process redesign projects, and the results suggest that the degree of communication flow orientation of a business process model is significantly related to the model's perceived quality.
Abstract
Business process redesign has been intensely studied, particularly since the mid 1990s. One aspect that received little attention, however, is the relationship between business process modeling choices and redesign success. This research gap is addressed through a multi-methods study of 18 business process redesign projects conducted in 18 different organizations. A structural equation model is developed and tested based on data collected from those projects; the results are then triangulated with qualitative data. The structural equation model depicts relationships between the following broad perceptual constructs: communication flow orientation of a business process model, quality of a business process model, and business process redesign success. The communication flow orientation of a business process model is defined as the extent to which a model explicitly shows how communication interactions take place in a process. A model's perceived quality is defined as the degree to which the model presents the following perceptual sub-constructs: ease of generation, ease of understanding, completeness, and accuracy. The results of the study suggest that the degree of communication flow orientation of a business process model is significantly related to the model's perceived quality. Perceived model quality, in turn, is significantly related to perceived business process redesign success. Interestingly, a business process model's perceived completeness does not seem to be influenced by a model's communication flow orientation. The structural equation model accounted for 56% of the explained variance in the business process redesign success construct. The main implication of this study is that a focus on communication flows in business processes is an important ingredient in successful business process redesign projects.

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Organizing information integration in agri-food: A method based on a service-oriented architecture and living lab approach

TL;DR: An overall method for analysis, design and implementation of information integration, taking technical as well as organizational development into account, which leads to ICT that follows the business processes in real life and thus enhances appropriate information sharing to support a knowledge-based economy.
Journal ArticleDOI

Making sense of business process descriptions: An experimental comparison of graphical and textual notations

TL;DR: This paper compares the success of university students in interpreting business process descriptions, for an established graphical notation (BPMN) and for an alternative textual notation (based on written use-cases)
Journal ArticleDOI

Factors of process model comprehension-Findings from a series of experiments

TL;DR: In this article, the authors use theories of semiotics and cognitive load to theorize how model and personal factors influence how model viewers comprehend the syntactical information of process models and report on a four-part series of experiments, in which they examined these factors.

Managing process model complexity via abstract syntax modifications

TL;DR: In this paper, the authors focus on complexity reduction mechanisms that affect the abstract syntax of a process model, i.e. the structure of a model, so that they can be described in their most general form and in a language and tool-independent manner.
References
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Journal ArticleDOI

Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
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Journal ArticleDOI

Multivariate data analysis

TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
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