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Inter-organizational success factors: a cause and effect model

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Abstract

Inter-organizational systems form the basis for successful business collaboration in the Internet and B2B e-commerce era. To properly design and manage such systems one needs to understand the structure and dynamics of the relationships between organizations. The evaluation of such inter-organizational relationships (IORs) is normally conducted using “success factors”. These are often referred to as constructs, such as trust and information sharing. In strategic management and performance analysis, different methods are employed for evaluating business performance and strategies, such as the Balanced Scorecard (BSC) method. The BSC utilizes success factors for measuring and monitoring IORs against business strategies. For these reasons, a thorough understanding of success factors, the relationships between them, as well as their relationship to business strategies is required. In other words, understanding success factors allows strategists deriving measurements for success factors as well as aligning these success factors with business strategies. This underpins nowadays close relationship between business strategy, IORs and their realization by means of inter-organizational systems. In this paper, we present (1) a systematic literature review studying success factors and their impact on IORs as well as (2) an analysis of the results found. The review is based on 177 publications, published between 2000 and 2012, dealing with factors influencing IORs. The work presented provides an overview on success factors, influencing relationships between success factors, as well as their influence on the success of IORs. The work is somehow “meta-empirical” as it only looks at published studies and not on own cases. Consequently, it is based on the assumption that studies in scientific literature represent the real-world. The constructs and relationships found in the review are grouped based on their scope and summarized in a cause and effect model. The grouping of constructs results in five groups including Relationship Orientation, Relational Norm, Relational Capital, Atmosphere, and Others. Since the cause and effect model represents a directed graph, different network analysis methods may be applied for analyzing the model. In particular, an in- and out-degree analysis is applied on the cause and effect model for detecting the most influencing as well as the most influenced success factors.

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Notes

  1. A list of reviewed studies is provided in our supplement which can be found at the following link: http://edimine.ec.tuwien.ac.at/supplement.

  2. A list of extracted influencing relationships is provided in the following supplement: http://edimine.ec.tuwien.ac.at/supplement.

  3. A list of refined influencing relationships can also be found in our provided supplement: http://edimine.ec.tuwien.ac.at/supplement.

  4. http://www.dlvsystem.com/.

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Acknowledgments

This research has been conducted in the context of the EDImine project and has been funded by the Vienna Science and Technology Fund (WWTF) through Project ICT10-010. Two of the authors of this paper were also supported by the Vienna Ph.D. School of Informatics (Krathu and Xiao) and the EU Project Optique FP7-318338 (Xiao).

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  1. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstrasse 9-11, 1040, Vienna, Austria

    Worarat Krathu, Christian Pichler, Hannes Werthner, Julia Neidhardt, Marco Zapletal & Christian Huemer

  2. Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani, 3, 39100, Bolzano, Italy

    Guohui Xiao

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  1. Worarat Krathu
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  2. Christian Pichler
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  3. Guohui Xiao
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  4. Hannes Werthner
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  6. Marco Zapletal
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Correspondence to Worarat Krathu or Guohui Xiao.

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Krathu, W., Pichler, C., Xiao, G. et al. Inter-organizational success factors: a cause and effect model. Inf Syst E-Bus Manage 13, 553–593 (2015). https://doi.org/10.1007/s10257-014-0258-z

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