business process research paper

Business Process Management Journal

• Submit your paper
• Author guidelines
• Editorial team
• Indexing & metrics
• Calls for papers & news

Before you start

For queries relating to the status of your paper pre decision, please contact the Editor or Journal Editorial Office. For queries post acceptance, please contact the Supplier Project Manager. These details can be found in the Editorial Team section.

Author responsibilities

Our goal is to provide you with a professional and courteous experience at each stage of the review and publication process. There are also some responsibilities that sit with you as the author. Our expectation is that you will:

• Respond swiftly to any queries during the publication process.
• Be accountable for all aspects of your work. This includes investigating and resolving any questions about accuracy or research integrity .
• Treat communications between you and the journal editor as confidential until an editorial decision has been made.
• Include anyone who has made a substantial and meaningful contribution to the submission (anyone else involved in the paper should be listed in the acknowledgements).
• Exclude anyone who hasn’t contributed to the paper, or who has chosen not to be associated with the research.
• In accordance with COPE’s position statement on AI tools , Large Language Models cannot be credited with authorship as they are incapable of conceptualising a research design without human direction and cannot be accountable for the integrity, originality, and validity of the published work. The author(s) must describe the content created or modified as well as appropriately cite the name and version of the AI tool used; any additional works drawn on by the AI tool should also be appropriately cited and referenced. Standard tools that are used to improve spelling and grammar are not included within the parameters of this guidance. The Editor and Publisher reserve the right to determine whether the use of an AI tool is permissible.
• If your article involves human participants, you must ensure you have considered whether or not you require ethical approval for your research, and include this information as part of your submission. Find out more about informed consent .

Generative AI usage key principles

• Copywriting any part of an article using a generative AI tool/LLM would not be permissible, including the generation of the abstract or the literature review, for as per Emerald’s authorship criteria, the author(s) must be responsible for the work and accountable for its accuracy, integrity, and validity.
• The generation or reporting of results using a generative AI tool/LLM is not permissible, for as per Emerald’s authorship criteria, the author(s) must be responsible for the creation and interpretation of their work and accountable for its accuracy, integrity, and validity.
• The in-text reporting of statistics using a generative AI tool/LLM is not permissible due to concerns over the authenticity, integrity, and validity of the data produced, although the use of such a tool to aid in the analysis of the work would be permissible.
• Copy-editing an article using a generative AI tool/LLM in order to improve its language and readability would be permissible as this mirrors standard tools already employed to improve spelling and grammar, and uses existing author-created material, rather than generating wholly new content, while the author(s) remains responsible for the original work.
• The submission and publication of images created by AI tools or large-scale generative models is not permitted.

Research and publishing ethics

Our editors and employees work hard to ensure the content we publish is ethically sound. To help us achieve that goal, we closely follow the advice laid out in the guidelines and flowcharts on the COPE (Committee on Publication Ethics) website .

We have also developed our research and publishing ethics guidelines . If you haven’t already read these, we urge you to do so – they will help you avoid the most common publishing ethics issues.

A few key points:

• Any manuscript you submit to this journal should be original. That means it should not have been published before in its current, or similar, form. Exceptions to this rule are outlined in our pre-print and conference paper policies .  If any substantial element of your paper has been previously published, you need to declare this to the journal editor upon submission. Please note, the journal editor may use  Crossref Similarity Check  to check on the originality of submissions received. This service compares submissions against a database of 49 million works from 800 scholarly publishers.
• Your work should not have been submitted elsewhere and should not be under consideration by any other publication.
• If you have a conflict of interest, you must declare it upon submission; this allows the editor to decide how they would like to proceed. Read about conflict of interest in our research and publishing ethics guidelines .
• By submitting your work to Emerald, you are guaranteeing that the work is not in infringement of any existing copyright.

Third party copyright permissions

Prior to article submission, you need to ensure you’ve applied for, and received, written permission to use any material in your manuscript that has been created by a third party. Please note, we are unable to publish any article that still has permissions pending. The rights we require are:

• Non-exclusive rights to reproduce the material in the article or book chapter.
• Print and electronic rights.
• Worldwide English-language rights.
• To use the material for the life of the work. That means there should be no time restrictions on its re-use e.g. a one-year licence.

We are a member of the International Association of Scientific, Technical, and Medical Publishers (STM) and participate in the STM permissions guidelines , a reciprocal free exchange of material with other STM publishers.  In some cases, this may mean that you don’t need permission to re-use content. If so, please highlight this at the submission stage.

Please take a few moments to read our guide to publishing permissions  to ensure you have met all the requirements, so that we can process your submission without delay.

Open access submissions and information

All our journals currently offer two open access (OA) publishing paths; gold open access and green open access.

If you would like to, or are required to, make the branded publisher PDF (also known as the version of record) freely available immediately upon publication, you can select the gold open access route once your paper is accepted. 

If you’ve chosen to publish gold open access, this is the point you will be asked to pay the APC (article processing charge) . This varies per journal and can be found on our APC price list or on the editorial system at the point of submission. Your article will be published with a Creative Commons CC BY 4.0 user licence , which outlines how readers can reuse your work.

Alternatively, if you would like to, or are required to, publish open access but your funding doesn’t cover the cost of the APC, you can choose the green open access, or self-archiving, route. As soon as your article is published, you can make the author accepted manuscript (the version accepted for publication) openly available, free from payment and embargo periods.

You can find out more about our open access routes, our APCs and waivers and read our FAQs on our open research page. 

Find out about open

Transparency and Openness Promotion (TOP) Guidelines

We are a signatory of the Transparency and Openness Promotion (TOP) Guidelines , a framework that supports the reproducibility of research through the adoption of transparent research practices. That means we encourage you to:

• Cite and fully reference all data, program code, and other methods in your article.
• Include persistent identifiers, such as a Digital Object Identifier (DOI), in references for datasets and program codes. Persistent identifiers ensure future access to unique published digital objects, such as a piece of text or datasets. Persistent identifiers are assigned to datasets by digital archives, such as institutional repositories and partners in the Data Preservation Alliance for the Social Sciences (Data-PASS).
• Follow appropriate international and national procedures with respect to data protection, rights to privacy and other ethical considerations, whenever you cite data. For further guidance please refer to our  research and publishing ethics guidelines . For an example on how to cite datasets, please refer to the references section below.

Prepare your submission

Manuscript support services.

We are pleased to partner with Editage, a platform that connects you with relevant experts in language support, translation, editing, visuals, consulting, and more. After you’ve agreed a fee, they will work with you to enhance your manuscript and get it submission-ready.

This is an optional service for authors who feel they need a little extra support. It does not guarantee your work will be accepted for review or publication.

Visit Editage

Manuscript requirements

Before you submit your manuscript, it’s important you read and follow the guidelines below. You will also find some useful tips in our structure your journal submission how-to guide.

Submit your manuscript

There are a number of key steps you should follow to ensure a smooth and trouble-free submission.

Double check your manuscript

Before submitting your work, it is your responsibility to check that the manuscript is complete, grammatically correct, and without spelling or typographical errors. A few other important points:

• Give the journal aims and scope a final read. Is your manuscript definitely a good fit? If it isn’t, the editor may decline it without peer review.
• Does your manuscript comply with our research and publishing ethics guidelines ?
• Have you cleared any necessary publishing permissions ?
• Have you followed all the formatting requirements laid out in these author guidelines?
• If you need to refer to your own work, use wording such as ‘previous research has demonstrated’ not ‘our previous research has demonstrated’.
• If you need to refer to your own, currently unpublished work, don’t include this work in the reference list.
• Any acknowledgments or author biographies should be uploaded as separate files.
• Carry out a final check to ensure that no author names appear anywhere in the manuscript. This includes in figures or captions.

You will find a helpful submission checklist on the website Think.Check.Submit .

The submission process

All manuscripts should be submitted through our editorial system by the corresponding author.

The only way to submit to the journal is through the journal’s ScholarOne site as accessed via the Emerald website, and not by email or through any third-party agent/company, journal representative, or website. Submissions should be done directly by the author(s) through the ScholarOne site and not via a third-party proxy on their behalf.

A separate author account is required for each journal you submit to. If this is your first time submitting to this journal, please choose the Create an account or Register now option in the editorial system. If you already have an Emerald login, you are welcome to reuse the existing username and password here.

Please note, the next time you log into the system, you will be asked for your username. This will be the email address you entered when you set up your account.

Don't forget to add your  ORCiD ID during the submission process. It will be embedded in your published article, along with a link to the ORCiD registry allowing others to easily match you with your work.

Don’t have one yet? It only takes a few moments to register for a free ORCiD identifier .

Visit the ScholarOne support centre  for further help and guidance.

What you can expect next

You will receive an automated email from the journal editor, confirming your successful submission. It will provide you with a manuscript number, which will be used in all future correspondence about your submission. If you have any reason to suspect the confirmation email you receive might be fraudulent, please contact the journal editor in the first instance.

Post submission

Review and decision process.

Each submission is checked by the editor. At this stage, they may choose to decline or unsubmit your manuscript if it doesn’t fit the journal aims and scope, or they feel the language/manuscript quality is too low.

If they think it might be suitable for the publication, they will send it to at least two independent referees for double anonymous peer review.  Once these reviewers have provided their feedback, the editor may decide to accept your manuscript, request minor or major revisions, or decline your work.

While all journals work to different timescales, the goal is that the editor will inform you of their first decision within 60 days.

During this period, we will send you automated updates on the progress of your manuscript via our submission system, or you can log in to check on the current status of your paper.  Each time we contact you, we will quote the manuscript number you were given at the point of submission. If you receive an email that does not match these criteria, it could be fraudulent and we recommend you contact the journal editor in the first instance.

Manuscript transfer service

Emerald’s manuscript transfer service takes the pain out of the submission process if your manuscript doesn’t fit your initial journal choice. Our team of expert Editors from participating journals work together to identify alternative journals that better align with your research, ensuring your work finds the ideal publication home it deserves. Our dedicated team is committed to supporting authors like you in finding the right home for your research.

If a journal is participating in the manuscript transfer program, the Editor has the option to recommend your paper for transfer. If a transfer decision is made by the Editor, you will receive an email with the details of the recommended journal and the option to accept or reject the transfer. It’s always down to you as the author to decide if you’d like to accept. If you do accept, your paper and any reviewer reports will automatically be transferred to the recommended journals. Authors will then confirm resubmissions in the new journal’s ScholarOne system.

Our Manuscript Transfer Service page has more information on the process.

If your submission is accepted

Open access.

Once your paper is accepted, you will have the opportunity to indicate whether you would like to publish your paper via the gold open access route.

If you’ve chosen to publish gold open access, this is the point you will be asked to pay the APC (article processing charge).  This varies per journal and can be found on our APC price list or on the editorial system at the point of submission. Your article will be published with a Creative Commons CC BY 4.0 user licence , which outlines how readers can reuse your work.

For UK journal article authors - if you wish to submit your work accepted by Emerald to REF 2021, you must make a ‘closed deposit’ of your accepted manuscript to your respective institutional repository upon acceptance of your article. Articles accepted for publication after 1st April 2018 should be deposited as soon as possible, but no later than three months after the acceptance date. For further information and guidance, please refer to the REF 2021 website.

All accepted authors are sent an email with a link to a licence form.  This should be checked for accuracy, for example whether contact and affiliation details are up to date and your name is spelled correctly, and then returned to us electronically. If there is a reason why you can’t assign copyright to us, you should discuss this with your journal content editor. You will find their contact details on the editorial team section above.

Proofing and typesetting

Once we have received your completed licence form, the article will pass directly into the production process. We will carry out editorial checks, copyediting, and typesetting and then return proofs to you (if you are the corresponding author) for your review. This is your opportunity to correct any typographical errors, grammatical errors or incorrect author details. We can’t accept requests to rewrite texts at this stage.

When the page proofs are finalised, the fully typeset and proofed version of record is published online. This is referred to as the EarlyCite version. While an EarlyCite article has yet to be assigned to a volume or issue, it does have a digital object identifier (DOI) and is fully citable. It will be compiled into an issue according to the journal’s issue schedule, with papers being added by chronological date of publication.

How to share your paper

Visit our author rights page  to find out how you can reuse and share your work.

To find tips on increasing the visibility of your published paper, read about  how to promote your work .

Correcting inaccuracies in your published paper

Sometimes errors are made during the research, writing and publishing processes. When these issues arise, we have the option of withdrawing the paper or introducing a correction notice. Find out more about our  article withdrawal and correction policies .

Need to make a change to the author list? See our frequently asked questions (FAQs) below.

Frequently asked questions

• Prof. Elena-Madalina Vatamanescu National University of Political Studies and Public Administration - Romania [email protected]

Associate Editor

• Prof. Dan-Cristian Dabija Babeș-Bolyai University - Romania
• Prof. Gandolfo Dominici University of Palermo - Business Systems Laboratory - Italy
• Associate Professor Aurora Martínez-Martínez Polytechnic University of Cartagena - Spain
• Asha Thomas Wrocław University of Science and Technology - Poland
• Professor Katarina Valaskova University of Zilina - Slovakia

Editorial Assistant

• Simona Popa University of Murcia - Spain [email protected]
• Joseph Johnson Emerald Publishing - UK [email protected]

Journal Editorial Office (For queries related to pre-acceptance)

• Poonam Sawant Emerald Publishing [email protected]

Supplier Project Manager (For queries related to post-acceptance)

• Preethi Vittal Emerald Publishing [email protected]

Editorial Advisory Board

• Professor Hassan Abdalla De Montfort University - UK
• Professor Frederic Adam University College Cork - Ireland
• Dr Hartini Ahmad Universiti Utara Malaysia - Malaysia
• Dr Davide Aloini University of Pisa - Italy
• Professor Mustafa Alshawi University of Salford - UK
• Professor Birdogan Baki Karadeniz Technical University - Turkey
• Professor Saad Haj Bakry King Saud University - Saudi Arabia
• Dr Ilia Bider Ibisoft AB - Sweden
• Professor Manlio Del Giudice Pegaso Digital University - Italy
• Dr Amit Deokar University of Massachusetts Lowell - USA
• Professor Georgios I Doukidis Athens University of Economics & Business - Greece
• Professor A Sharaf Eldin Helwan University - Egypt
• Samuel Fosso Wamba Ph.D. Department of Information, Operations, and Management Sciences, Universite de Toulouse - France
• Dr Ilse Geldenhuys University of Pretoria - South Africa
• Professor Angappa Gunasekaran Penn State Harrisburg - USA
• Professor Suliman Hawamdeh College of Information, University of North Texas - USA
• Professor Dr Bernd Heinrich University of Regensburg - Germany
• Professor Zahir Irani University of Bradford - UK
• Associate Professor Adam Jablonski WSB University in Poznan - Poland
• Professor Mahadeo P Jaiswal Management Development Institute Gurgaon - India
• Professor Kai Jakobs Technical University of Aachen - Germany
• Dr Ismail Khalil Johannes Kepler University Linz, Austria - Austria
• Dr Gyeung-Min Kim Ewha Womans University - South Korea
• Dr Peter Küng IT Architecture and Standards - Switzerland
• Dr Ming-Fong Lai National Applied Research Laboratories - Taiwan (Republic of China)
• Professor Binshan Lin Louisiana State University in Shreveport - USA
• Professor Jan Mendling Vienna University of Economics and Business Administration - Austria
• Dr Nawaz Mohamudally University of Technology - Mauritius
• Professor Aihie Osarenkhoe University of Gävle - Sweden
• Professor Rafael Paim Cefet-RJ DEPRO/DEPES - Brazil
• Dr Thomás F. Espino Rodríguez University of Las Palmas de Gran Canaria - Spain
• Michael Rosemann Queensland University of Technology - Australia
• Mr Christopher Seow University of Bath - UK
• Dr Afzaal H Seyal Institute of Technology Brunei - Brunei Darussalam
• Distinguished Professor Muzaffar Shaikh Florida Institute of Technology - USA
• Professor Namchul Shin Pace University School of CSIS - USA
• Prof Togar M Simatupang Bandung Institute of Technology - Indonesia
• Dr Kimberlee D Snyder Winona State University - USA
• Dr Khalid S Soliman Hofstra University - USA
• Dr Mohamed Tounsi Prince Sultan University - Saudi Arabia
• Dr Zulkifli Mohamed Udin University Utara Malaysia - Malaysia
• Professor Christian Wagner City University of Hong Kong - Hong Kong
• Professor Anthony Wensley Rotman School of Business - Canada

Founding Editor

• Professor Mohamed Zairi () Bradford University School of Management - UK [email protected]

Editorial Advisory Group

• Professor Thomas Davenport Babson College - USA
• Professor Varun Grover University of Arkansas - USA
• Dr H James Harrington The Harrington Institute - USA
• Professor N Venkat Venkatraman Boston University - USA

Citation metrics

CiteScore 2023

Further information

CiteScore is a simple way of measuring the citation impact of sources, such as journals.

Calculating the CiteScore is based on the number of citations to documents (articles, reviews, conference papers, book chapters, and data papers) by a journal over four years, divided by the number of the same document types indexed in Scopus and published in those same four years.

For more information and methodology visit the Scopus definition

CiteScore Tracker 2024

(updated monthly)

CiteScore Tracker is calculated in the same way as CiteScore, but for the current year rather than previous, complete years.

The CiteScore Tracker calculation is updated every month, as a current indication of a title's performance.

2023 Impact Factor

The Journal Impact Factor is published each year by Clarivate Analytics. It is a measure of the number of times an average paper in a particular journal is cited during the preceding two years.

For more information and methodology see Clarivate Analytics

5-year Impact Factor (2023)

A base of five years may be more appropriate for journals in certain fields because the body of citations may not be large enough to make reasonable comparisons, or it may take longer than two years to publish and distribute leading to a longer period before others cite the work.

Actual value is intentionally only displayed for the most recent year. Earlier values are available in the Journal Citation Reports from Clarivate Analytics .

Publication timeline

Time to first decision

Time to first decision , expressed in days, the "first decision" occurs when the journal’s editorial team reviews the peer reviewers’ comments and recommendations. Based on this feedback, they decide whether to accept, reject, or request revisions for the manuscript.

Data is taken from submissions between 1st June 2023 and 31st May 2024

Acceptance to publication

Acceptance to publication , expressed in days, is the average time between when the journal’s editorial team decide whether to accept, reject, or request revisions for the manuscript and the date of publication in the journal. 

Data is taken from the previous 12 months (Last updated July 2024)

Acceptance rate

The acceptance rate is a measurement of how many manuscripts a journal accepts for publication compared to the total number of manuscripts submitted expressed as a percentage %

Data is taken from submissions between 1st June 2023 and 31st May 2024 .

This figure is the total amount of downloads for all articles published early cite in the last 12 months

(Last updated: July 2024)

This journal is abstracted and indexed by

• Business Source Alumni Edition/Complete/Complete: Government Edition/Corporate/Corporate Plus/Elite/Main Edition/Premier
• Cabell's Dictionary of Publishing Opportunities in Management and Marketing
• Contents Pages in Management
• ISI: Social Science Citation Index
• ReadCube Discover
• TOC Premier (EBSCO)
• VHB-JOURQUAL 3

This journal is ranked by

• Association of Business Schools (ABS) Academic Journal Quality Guide
• AERES (France)
• Australian Business Deans Council (ABDC) Quality Journal List
• Australian Research Council (ERA Journal List)
• BFI (Denmark)
• CNRS (France)
• FMS Journal Rating Guide - C (China)
• FNEGE (France)
• JourQUAL 2.1 (Germany)
• NSD (Norway)
• Polish Scholarly Bibliography (PBN)
• Qualis (Brazil)
• The Publication Forum (Finland)
• VHB Publication Media Rating 2024 (Germany) - Level C

Reviewer information

Peer review process.

This journal engages in a double-anonymous peer review process, which strives to match the expertise of a reviewer with the submitted manuscript. Reviews are completed with evidence of thoughtful engagement with the manuscript, provide constructive feedback, and add value to the overall knowledge and information presented in the manuscript.

The mission of the peer review process is to achieve excellence and rigour in scholarly publications and research.

Our vision is to give voice to professionals in the subject area who contribute unique and diverse scholarly perspectives to the field.

The journal values diverse perspectives from the field and reviewers who provide critical, constructive, and respectful feedback to authors. Reviewers come from a variety of organizations, careers, and backgrounds from around the world.

All invitations to review, abstracts, manuscripts, and reviews should be kept confidential. Reviewers must not share their review or information about the review process with anyone without the agreement of the editors and authors involved, even after publication. This also applies to other reviewers’ “comments to author” which are shared with you on decision.

Resources to guide you through the review process

Discover practical tips and guidance on all aspects of peer review in our reviewers' section. See how being a reviewer could benefit your career, and discover what's involved in shaping a review.

More reviewer information

Calls for papers

Digital innovation and business process management: advancing theory and practice.

Submit your paper here! Introduction Recent studies by McKinsey & Co. (2022; 2024) highlight that companies worldwide spend an estimated $230...

Thank you to the 2022 Reviewers of Business Process Management Journal

The publishing and editorial teams would like to thank the following, for their invaluable service as 2022 reviewers for this journal. We are very grateful for the contributions made. With their help, the journal has been able to publish such high...

Thank you to the 2021 Reviewers of Business Process Management Journal

The publishing and editorial teams would like to thank the following, for their invaluable service as 2021 reviewers for this journal. We are very grateful for the contributions made. With their help, the journal has been able to publish such high...

Thank you to the 2019 Reviewers for Business Process Management Journal (BPMJ)

The academic process as we know it could not exist without the service you provide. We are grateful for your continued support of the journal: Majid Aarabi Sherief Abdallah Neda Abdolvand Fred Adam Amir Afrasi...

Literati awards

Business Process Management Journal - Literati Award Winners 2022

We are pleased to announce our 2022 Literati Award winners. Outstanding Paper Using e-wallet for business p...

Business Process Management Journal (BPMJ) examines how a variety of business processes intrinsic to organizational efficiency and effectiveness are integrated and managed for competitive success and in so doing, disseminates best practice.

Aims and scope

Business processes are a fundamental building block of organizational success. Even though effectively managing business process is a key activity for business prosperity, there remain considerable gaps in understanding how to drive efficiency through a process approach. Building a clear and deep understanding of the range process, how they function, and how to manage them is the major challenge facing modern business.

Business Process Management Journal (BPMJ) examines how a variety of business processes intrinsic to organizational efficiency and effectiveness are integrated and managed for competitive success.

Coverage includes:

• BPM in eBusiness, eCommerce and eGovernment 
• Web-based enterprise application integration 
• eBPM, ERP, CRM, ASP & SCM 
• Knowledge management and learning organization
• Methodologies, techniques and tools of business process modeling, analysis and design 
• Techniques of moving from one-shot business process re-engineering to continuous improvement 
• Best practices in BPM 
• Performance management 
• Tools and techniques of change management 
• BPM case studies

Latest articles

These are the latest articles published in this journal (Last updated: July 2024)

Green Supply Chain Management and Sustainable Performance: Exploring the role of Green Ambidexterity Innovation and Top Management Environmental Awareness

Strengthening green competitive advantage through organizational learning and green marketing capabilities in a big data environment: a moderated-mediation model, adopting industry 4.0 through absorptive capacity and innovation ambidexterity with the moderation of learning capability, top downloaded articles.

These are the most downloaded articles over the last 12 months for this journal (Last updated: July 2024)

Developing a business intelligence tool for sustainability management

Critical success factors for change management in business process management, creating ai business value through bpm capabilities.

These are the top cited articles for this journal, from the last 12 months according to Crossref (Last updated: July 2024)

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Identifying the factors influencing enterprise digital transformation intention: an empirical study based on net effects and joint effects, esg performance and firms' innovation efficiency: the moderating role of state-owned firms and regional market development.

This journal is aligned with our responsible management goal

We aim to champion researchers, practitioners, policymakers and organisations who share our goals of contributing to a more ethical, responsible and sustainable way of working.

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• Open access
• Published: 18 October 2016

Business process performance measurement: a structured literature review of indicators, measures and metrics

• Amy Van Looy   ORCID: orcid.org/0000-0002-7992-1528 1 &
• Aygun Shafagatova 1  

SpringerPlus volume  5 , Article number:  1797 ( 2016 ) Cite this article

44k Accesses

84 Citations

3 Altmetric

Metrics details

Measuring the performance of business processes has become a central issue in both academia and business, since organizations are challenged to achieve effective and efficient results. Applying performance measurement models to this purpose ensures alignment with a business strategy, which implies that the choice of performance indicators is organization-dependent. Nonetheless, such measurement models generally suffer from a lack of guidance regarding the performance indicators that exist and how they can be concretized in practice. To fill this gap, we conducted a structured literature review to find patterns or trends in the research on business process performance measurement. The study also documents an extended list of 140 process-related performance indicators in a systematic manner by further categorizing them into 11 performance perspectives in order to gain a holistic view. Managers and scholars can consult the provided list to choose the indicators that are of interest to them, considering each perspective. The structured literature review concludes with avenues for further research.

Since organizations endeavor to measure what they manage, performance measurement is a central issue in both the literature and in practice (Heckl and Moormann 2010 ; Neely 2005 ; Richard et al. 2009 ). Performance measurement is a multidisciplinary topic that is highly studied by both the management and information systems domains (business process management or BPM in particular). Different performance measurement models, systems and frameworks have been developed by academia and practitioners (Cross and Lynch 1988 ; Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Kueng 2000 ; Neely et al. 2000 ). While measurement models were initially limited to financial performance (e.g., traditional controlling models), a more balanced and integrated approach was needed beginning in the 1990s due to the challenges of the rapidly changing society and technology; this approach resulted in multi-dimensional models. Perhaps the best known multi-dimensional performance measurement model is the Balanced Scorecard (BSC) developed by Kaplan and Norton ( 1996 , 2001 ), which takes a four-dimensional approach to organizational performance: (1) financial perspective, (2) customer perspective, (3) internal business process perspective, and (4) “learning and growth” perspective. The BSC helps translate an organization’s strategy into operational performance indicators (also called performance measures or metrics) and objectives with targets for each of these performance perspectives. Even today, the BSC is by far the most used performance measurement approach in the business world (Bain Company 2015 ; Sullivan 2001 ; Ulfeder 2004 ).

Equally important for measuring an organization’s performance is process-oriented management or business process management (BPM), which is “about managing entire chains of events, activities and decisions that ultimately add value to the organization and its customers. These ‘chains of events, activities and decisions’ are called processes” (Dumas et al. 2013 : p. 1). In particular, an organization can do more with its current resources by boosting the effectiveness and efficiency of its way of working (i.e., its business processes) (Sullivan 2001 ). In this regard, academic research also suggests a strong link between business process performance and organizational performance, either in the sense of a causal relationship (Melville et al. 2004 ; Smith and Reece 1999 ) or as distinctive indicators that co-exist, as in the BSC (Kaplan and Norton 1996 , 2001 ).

Nonetheless, performance measurement models tend to give little guidance on how business (process) performance indicators can be chosen and operationalized (Shah et al. 2012 ). They are limited to mainly defining performance perspectives, possibly with some examples or steps to derive performance indicators (Neely et al. 2000 ), but without offering concrete indicators. Whereas fairly large bodies of research exist for both performance models and business processes, no structured literature review of (process) performance measurement has been carried out thus far. To the best of our knowledge, existing reviews cover one or another aspect of performance measurement; for instance, reviews on measurement models or evaluation criteria for performance indicators (Heckl and Moormann 2010 ; Neely 2005 ; Richard et al. 2009 ). Despite the considerable importance of a comprehensive and holistic approach to business (process) performance measurement, little is known regarding the state of the research on alternative performance indicators and their operationalization with respect to evaluating the performance of an organization’s work routines. To some extent, this lack of guidance can be explained by the fact that performance indicators are considered organization-dependent, given that strategic alignment is claimed by many measurement models such as the BSC (Kaplan and Norton 1996 , 2001 ). Although the selection of appropriate performance indicators is challenging for practitioners due to the lack of best practices, it is also highly relevant for performance measurement.

The gap that we are studying is the identification and, in particular, the concretization/operationalization of process-related performance indicators. This study enhances the information systems literature, which focuses on the design and development of measurement systems without paying much attention to essential indicators. To fill this gap, our study presents a structured literature review in order to describe the current state of business process performance measurement and related performance indicators. The choice to focus on the business process management (BPM) discipline is motivated by the close link between organizational performance and business process performance, as well as to ensure a clear scope (specifically targeting an organization’s way of working). Accordingly, the study addresses the following research questions.

RQ1. What is the current state of the research on business process performance measurement?

RQ2. Which indicators, measures and metrics are used or mentioned in the current literature related to business process performance?

The objective of RQ1 is to identify patterns in the current body of knowledge and to note weaknesses, whereas RQ2 mainly intends to develop an extended list of measurable process performance indicators, categorized into recognized performance perspectives, which can be tailored to diverse purposes. This list could, for instance, serve as a supplement to existing performance measurement models. Practitioners can use the list as a source for best practice indicators from academic research to find and select a subset of performance indicators that fit their strategy. The study will thus not address the development of specific measurement systems but rather the indicators to be used within such systems. To make our intended list system-independent, we will begin with the BSC approach and extend its performance perspectives. Given this generic approach, the research findings can also be used by scholars when building and testing theoretical models in which process performance is one of the factors that must be concretized.

The remainder of this article is structured as follows. “ Theoretical background ” section describes the theoretical background of performance measurement models and performance indicators. Next, the methodology for our structured literature review is detailed in “ Methods ” section. The subsequent sections present the results for RQ1 (“ Results for RQ1 ” section) and RQ2 (“ Results for RQ2 ” section). The discussion of the results in provided in “ Discussion ” section, followed by concluding comments (“ Conclusion ” section).

Theoretical background

This section addresses the concepts of performance measurement models and performance indicators separately in order to be able to differentiate them further in the study.

Performance measurement models

According to overviews in the performance literature (Heckl and Moormann 2010; Neely 2005 ; Richard et al. 2009 ), some of the most cited performance measurement models are the Balanced Scorecard (Kaplan and Norton 1996 , 2001 ), self-assessment excellence models such as the EFQM ( 2010 ), and the models by Cross and Lynch ( 1988 ), Kueng ( 2000 ) and Neely et al. ( 2000 ). A distinction should, however, be made between models focusing on the entire business (Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Cross and Lynch 1988 ) and models focusing on a single business process (Kueng 2000 ; Neely et al. 2000 ).

Organizational performance measurement models

Organizational performance measurement models typically intend to provide a holistic view of an organization’s performance by considering different performance perspectives. As mentioned earlier, the BSC provides four perspectives for which objectives and performance indicators ensure alignment between strategies and operations (Fig.  1 ) (Kaplan and Norton 1996 , 2001 ). Other organizational performance measurement models provide similar perspectives. For instance, Cross and Lynch ( 1988 ) offer a four-level performance pyramid: (1) a top level with a vision, (2) a second level with objectives per business unit in market and financial terms, (3) a third level with objectives per business operating system in terms of customer satisfaction, flexibility and productivity, and (4) a bottom level with operational objectives for quality, delivery, process time and costs. Another alternative view on organizational performance measurement is given in business excellence models, which focus on an evaluation through self-assessment rather than on strategic alignment, albeit by also offering performance perspectives. For instance, the EFQM ( 2010 ) distinguishes enablers [i.e., (1) leadership, (2) people, (3) strategy, (4) partnerships and resources, and (5) processes, products and services] from results [i.e., (1) people results, (2) customer results, (3) society results, and (4) key results], and a feedback loop for learning, creativity and innovation.

An overview of the performance perspectives in Kaplan and Norton ( 1996 , 2001 )

Since the BSC is the most used performance measurement model, we have chosen it as a reference model to illustrate the function of an organizational performance measurement model (Kaplan and Norton 1996 , 2001 ). The BSC is designed to find a balance between financial and non-financial performance indicators, between the interests of internal and external stakeholders, and between presenting past performance and predicting future performance. The BSC encourages organizations to directly derive (strategic) long-term objectives from the overall strategy and to link them to (operational) short-term targets. Concrete performance measures or indicators should be defined to periodically measure the objectives. These indicators are located on one of the four performance perspectives in Fig.  1 (i.e., ideally with a maximum of five indicators per perspective).

Table  1 illustrates how an organizational strategy can be translated into operational terms using the BSC.

During periodical measurements using the BSC, managers can assign color-coded labels according to actual performance on short-term targets: (1) a green label if the organization has achieved the target, (2) an orange label if it is almost achieved, or (3) a red label if it is not achieved. Orange and red labels thus indicate areas for improvement.

Furthermore, the BSC assumes a causal or logical relationship between the four performance perspectives. An increase in the competences of employees (i.e., performance related to “learning and growth”) is expected to positively affect the quality of products and services (i.e., internal business process performance), which in turn will lead to improved customer perceptions (i.e., customer performance). The results for the previous perspectives will then contribute to financial performance to ultimately realize the organization’s strategy, mission and vision (Kaplan and Norton 1996 , 2001 ). Hence, indicators belonging to the financial and customer perspectives are assumed to measure performance outcomes, whereas indicators from the perspectives of internal business processes and “learning and growth” are considered as typical performance drivers (Kaplan and Norton 2004 ).

Despite its widespread use and acceptance, the BSC is also criticized for appearing too general by managers who are challenged to adapt it to the culture of their organization (Butler et al. 1997 ) or find suitable indicators to capture the various aspects of their organization’s strategy (Shah et al. 2012 ; Vaivio 1999 ). Additionally, researchers question the choice of four distinct performance perspectives (i.e., which do not include perspectives related to inter-organizational performance or sustainability issues) (EFQM 2010 ; Hubbard 2009 , Kueng 2000 ). Further, the causal relationship among the BSC perspectives has been questioned (Norreklit 2000 ). To some degree, Kaplan and Norton ( 2004 ) responded to this criticism by introducing strategy maps that focus more on the causal relationships and the alignment of intangible assets.

Business process performance measurement models

In addition to organizational models, performance measurement can also focus on a single business process, such as statistical process control, workflow-based monitoring or process performance measurement systems (Kueng 2000 ; Neely et al. 2000 ). The approach taken in business process performance measurement is generally less holistic than the BSC. For instance, in an established BPM handbook, Dumas et al. ( 2013 ) position time, cost, quality and flexibility as the typical performance perspectives of business process performance measurement (Fig.  2 ). Similar to organizational performance measurement, concrete performance measures or indicators should be defined for each process performance perspective. In this sense, the established perspectives of Dumas et al. ( 2013 ) seem to further refine the internal business process performance perspective of the BSC.

An overview of the performance perspectives in Dumas et al. ( 2013 )

Neely et al. ( 2000 ), on the other hand, present ten steps to develop or define process performance indicators. The process performance measurement system of Kueng ( 2000 ) is also of high importance, which is visualized as a “goal and performance indicator tree” with five process performance perspectives: (1) financial view, (2) customer view, (3) employee view, (4) societal view, and (5) innovation view. Kueng ( 2000 ) thus suggests a more holistic approach towards process performance, similar to organizational performance, given the central role of business processes in an organization. He does so by focusing more on the different stakeholders involved in certain business processes.

Performance indicators

Section “ Performance measurement models ” explained that performance measurement models typically distinguish different performance perspectives for which performance indicators should be further defined. We must, however, note that we consider performance measures, performance metrics and (key) performance indicators as synonyms (Dumas et al. 2013 ). For reasons of conciseness, this work will mainly refer to performance indicators without mentioning the synonyms. In addition to a name, each performance indicator should also have a concretization or operationalization that describes exactly how it is measured and that can result in a value to be compared against a target. For instance, regarding the example in Table  1 , the qualitative statements to measure customer satisfaction constitute an operationalization. Nonetheless, different ways of operationalization can be applied to measure the same performance indicator. Since organizations can profit from reusing existing performance indicators and the related operationalization instead of inventing new ones (i.e., to facilitate benchmarking and save time), this work investigates which performance indicators are used or mentioned in the literature on business process performance and how they are operationalized.

Neely et al. ( 2000 ) and Richard et al. ( 2009 ) both present evaluation criteria for performance indicators (i.e., in the sense of desirable characteristics or review implications), which summarize the general consensus in the performance literature. First, the literature strongly agrees that performance indicators are organization-dependent and should be derived from an organization’s objectives, strategy, mission and vision. Secondly, consensus in the literature also exists regarding the need to combine financial and non-financial performance indicators. Nonetheless, disagreement still seems to exist in terms of whether objective and subjective indicators need to be combined, with objective indicators preferred by most advocates. Although subjective (or quasi-objective) indicators face challenges from bias, their use has some advantages; for instance, to include stakeholders in an assessment, to address latent constructs or to facilitate benchmarking when a fixed reference point is missing (Hubbard 2009 ; Richard et al. 2009 ). Moreover, empirical research has shown that subjective (or quasi-objective) indicators are more or less correlated with objective indicators, depending on the level of detail of the subjective question (Richard et al. 2009 ). For instance, a subjective question can be made more objective by using clear definitions or by selecting only well-informed respondents to reduce bias.

We conducted a structured literature review (SLR) to find papers dealing with performance measurement in the business process literature. SLR can be defined as “a means of evaluating and interpreting all available research relevant to a particular research question, topic area, or phenomenon of interest” (Kitchenham 2007 : p. vi). An SLR is a meta study that identifies and summarizes evidence from earlier research (King and He 2005 ) or a way to address a potentially large number of identified sources based on a strict protocol used to search and appraise the literature (Boellt and Cecez-Kecmanovic 2015 ). It is systematic in the sense of a systematic approach to finding relevant papers and a systematic way of classifying the papers. Hence, according to Boellt and Cecez-Kecmanovic ( 2015 ), SLR as a specific type of literature review can only be used when two conditions are met. First, the topic should be well-specified and closely formulated (i.e., limited to performance measurement in the context of business processes) to potentially identify all relevant literature based on inclusion and exclusion criteria. Secondly, the research questions should be answered by extracting and aggregating evidence from the identified literature based on a high-level summary or bibliometric-type of content analysis. Furthermore, King and He ( 2005 ) also refer to a statistical analysis of existing literature.

Informed by the established guidelines proposed by Kitchenham ( 2007 ), we undertook the review in distinct stages: (1) formulating the research questions and the search strategy, (2) filtering and extracting data based on inclusion and exclusion criteria, and (3) synthesizing the findings. The remainder of this section describes the details of each stage.

Formulating the research questions and search strategy

A comprehensive and unbiased search is one of the fundamental factors that distinguish a systematic review from a traditional literature review (Kitchenham 2007 ). For this purpose, a systematic search begins with the identification of keywords and search terms that are derived from the research questions. Based on the research questions stipulated in the introduction, the SLR protocol (Boellt and Cecez-Kecmanovic 2015 ) for our study was defined, as shown in Table  2 .

The ISI Web of Science (WoS) database was searched using predetermined search terms in November 2015. This database was selected because it is used by many universities and results in the most outstanding publications, thus increasing the quality of our findings. An important requirement was that the papers focus on “business process*” (BP). This keyword was used in combination with at least one of the following: (1) “performance indicator*”, (2) “performance metric*”, (3) “performance measur*”. All combinations of “keyword in topic” (TO) and “keyword in title” (TI) have been used.

Table  3 shows the degree to which the initial sample sizes varied, with 433 resulting papers for the most permissive search query (TOxTO) and 19 papers for the most restrictive one (TIxTI). The next stage started with the most permissive search query in an effort to select and assess as many relevant publications as possible.

Filtering and extracting data

Figure  3 summarizes the procedure for searching and selecting the literature to be reviewed. The list of papers found in the previous stage was filtered by deleting 35 duplicates, and the remaining 398 papers were further narrowed to 153 papers by evaluating their title and abstract. After screening the body of the texts, 76 full-text papers were considered relevant for our scope and constituted the final sample (“Appendix 1 ”).

Exclusion of papers and number of primary studies

More specifically, studies were excluded if their main focus was not business process performance measurement or if they did not refer to indicators, measures or metrics for business performance. The inclusion of studies was not restricted to any specific type of intervention or outcome. The SLR thus included all types of research studies that were written in English and published up to and including November 2015. Furthermore, publication by peer-reviewed publication outlets (e.g., journals or conference proceedings) was considered as a quality criterion to ensure the academic level of the research papers.

Synthesizing the findings

The analysis of the final sample was performed by means of narrative and descriptive analysis techniques. For RQ1, the 76 papers were analyzed on the basis of bibliometric data (e.g., publication type, publication year, geography) and general performance measurement issues by paying attention to the methodology and focus of the study. Details are provided in “Appendix 2 ”.

For RQ2, all the selected papers were screened to identify concrete performance indicators in order to generate a comprehensive list or checklist. The latter was done in different phases. In the first phase, the structured literature review allowed us to analyze which performance indicators are mainly used in the process literature and how they are concretized (e.g., in a question or mathematical formulation), resulting in an unstructured list of potential performance indicators. The indicators were also synthesized by combining similar indicators and rephrasing them into more generic terms.

The next phase was a comparative study to categorize the output of phase 1 into the commonly used measurement models in the performance literature (see “ Theoretical background ” section). For the purpose of this study, we specifically looked for those organizational performance models, mentioned in “ Theoretical background ” section, that are cited the most and that suggest categories, dimensions or performance perspectives that can be re-used (Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Cross and Lynch 1988 ; Kueng 2000 ). Since the BSC (Kaplan and Norton 1996 , 2001 ) is the most commonly used of these measurement models, we began with the BSC as the overall framework to categorize the observed indicators related to business (process) performance, supplemented with an established view on process performance from the process literature (Dumas et al. 2013 ). Subsequently, a structured list of potential performance indicators was obtained.

In the third and final phase, an evaluation study was performed to validate whether the output of phase 2 is sufficiently comprehensive according to other performance measurement models, i.e., not included in our sample and differing from the most commonly used performance measurement models. Therefore, we investigated the degree to which our structured list covers the items in two variants or concretizations of the BSC. Hence, a validation by other theoretical models is provided. We note that a validation by subject-matter experts is out of scope for a structured literature review but relates to an opportunity for further research.

Results for RQ1

The final sample of 76 papers consists of 46 journal papers and 30 conference papers (Fig.  4 ), indicating a wide variety of outlets to reach the audience via operations and production-related journals in particular or in lower-ranked (Recker 2013 ) information systems journals.

The distribution of the sampled papers per publication type (N = 76)

When considering the chronological distribution of the sampled papers, Fig.  5 indicates an increase in the uptake of the topic in recent years, particularly for conference papers but also for journal publications since 2005.

The chronological distribution of the sampled papers per publication type (N = 76)

This uptake seems particularly situated in the Western world and Asia (Fig.  6 ). The countries with five or more papers in our sample are Germany (12 papers), the US (6 papers), Spain (5 papers), Croatia (5 papers) and China (5 papers). Figure  6 shows that business process performance measurement is a worldwide topic, with papers across the different continents. Nonetheless, a possible explanation for the higher coverage in the Western world could be due to its long tradition of measuring work (i.e., BSC origins).

The geographical distribution of the sampled papers per continent, based on a paper’s first author (N = 76)

The vast majority of the sampled papers address artifacts related to business (process) performance measurement. When looking at the research paradigm in which the papers are situated (Fig.  7 ), 71 % address design-science research, whereas 17 % conduct research in behavioral science and 12 % present a literature review. This could be another explanation for the increasing uptake in the Western world, as many design-science researchers are from Europe or North America (March and Smith 1995 ; Peffers et al. 2012 ).

The distribution of the sampled journal papers per research paradigm (N = 76)

Figure  8 supplements Fig.  7 by specifying the research methods used in the papers. For the behavioral-science papers, case studies and surveys are equally used. The 54 papers that are situated within the design-science paradigm explicitly refer to models, meta-models, frameworks, methods and/or tools. When mapping these 54 papers to the four artifact types of March and Smith ( 1995 ), the vast majority present (1) methods in the sense of steps to perform a task (e.g., algorithms or guidelines for performance measurement) and/or (2) models to describe solutions for the topic. The number of papers dealing with (3) constructs or a vocabulary and/or (4) instantiations or tools is much more limited, with 14 construct-related papers and 9 instantiations in our sample. We also looked at which evaluation methods, defined by Peffers et al. ( 2012 ), are typically used in the sampled design-science papers. While 7 of the 54 design-science papers do not seem to report on any evaluation effort, our sample confirms that most papers apply one or another evaluation method. Case studies and illustrative scenarios appear to be the most frequently used methods to evaluate design-science research on business (process) performance measurement.

The distribution of the sampled journal papers per research method (N = 76)

The sampled design-science research papers typically build and test performance measurement frameworks, systems or models or suggest meta-models and generic templates to integrate performance indicators into the process models of an organization. Such papers can focus on the process level, organizational level or even cross-organizational level. Nonetheless, the indicators mentioned in those papers are illustrative rather than comprehensive. An all-inclusive list of generic performance indicators seems to be missing. Some authors propose a set of indicators, but those indicators are specific to a certain domain or sector instead of being generic. For instance, Table  4 shows that 36 of the 76 sampled papers are dedicated to a specific domain or sector, such as technology-related aspects or supply chain management.

Furthermore, the reviewed literature was analyzed with regard to its (1) scope, (2) functionalities, (3) terminology, and (4) foundations.

Starting with scope, it is observed that nearly two-thirds of the sampled papers can be categorized as dealing with process-oriented performance measurement, whereas one-third focuses more on general performance measurement and management issues. Nonetheless, most of the studies of process performance also include general performance measurement as a supporting concept. A minor cluster of eight research papers specifically focuses on business process reengineering and measurement systems to evaluate the results of reengineering efforts. Furthermore, other researchers focus on the measurement and assessment of interoperability issues and supply chain management measurements.

Secondly, while analyzing the literature, two groups of papers were identified based on their functionalities: (1) focusing on performance measurement systems or frameworks, and (2) focusing on certain performance indicators and their categorization. Regarding the first group, it should be mentioned that while the process of building or developing a performance measurement system (PMS) or framework is well-researched, only a small number of papers explicitly address process performance measurement systems (PPMS). The papers in this first group typically suggest concrete steps or stages to be followed by particular organizations or discuss the conceptual characteristics and design of a performance measurement system. Regarding the second group of performance indicators, we can differentiate two sub-groups. Some authors focus on the process of defining performance indicators by listing requirements or quality characteristics that an indicator should meet. However, many more authors are interested in integrating performance indicators into the process models or the whole architecture of an organization, and they suggest concrete solutions to do so. Compared to the first group of papers, this second group deals more with the categorization of performance indicators into domains (financial/non-financial, lag/lead, external/internal, BSC dimensions) or levels (strategic, tactical, operational).

Thirdly, regarding terminology, different terms are used by different authors to discuss performance measurement. Performance “indicator” is the most commonly used term among the reviewed papers. For instance, it is frequently used in reference to a key performance indicator (KPI), a KPI area or a performance indicator (PI). The concept of a process performance indicator (PPI) is also used, mainly in the process-oriented literature. Performance “measure” is another prevalent term in the papers. The least-used term is performance “metric” (i.e., in only nine papers). Although the concepts of performance indicators, measures and metrics are used interchangeably throughout most of the papers, the concepts are sometimes defined in different ways. For instance, paper 17 defines a performance indicator as a metric, and paper 49 defines a performance measure as an indicator. On the other hand, paper 7 defines a performance indicator as a set of measures. Yet another perspective is taken in paper 74, which defines a performance measure as “a description of something that can be directly measured (e.g., number of reworks per day)”, while defining a performance indicator as “a description of something that is calculated from performance measures (e.g., percentage reworks per day per direct employee” (p. 386). Inconsistencies exist not only in defining indicators but also in describing performance goals. For instance, some authors include a sign (e.g., minus or plus) or a verb (e.g., decrease or increase) in front of an indicator. Other authors attempt to describe performance goals in a SMART way—for instance, by including a time indication (e.g., “within a certain period”) and/or target (e.g., “5 % of all orders”)—whereas most of the authors are less precise. Hence, a great degree of ambiguity exists in the formulation of performance objectives among to the reviewed papers.

Finally, regarding the papers’ foundations, “ Performance measurement models ” section already indicated that the BSC plays an important role in the general literature on performance management systems (PMS), while Kueng ( 2000 ) also offers influential arguments on process performance measurement systems (PPMS). In our literature review, we observed that the BSC was mentioned in 43 of the 76 papers and that the results of 19 papers were mainly based on the BSC (Fig.  9 ). This finding provides additional evidence that the BSC can be considered the most frequently used performance model in academia as well. However, the measurement model of Kueng ( 2000 ) was also mentioned in the sampled papers on PPMS, though less frequently (i.e., in six papers).

The importance of the BSC according to the sampled papers (N = 76)

Interestingly, the BSC is also criticized by the sampled papers for not being comprehensive; for instance, due to the exclusion of environmental aspects, supply chain management aspects or cross-organizational processes. In response, some of the sampled papers also define sector-specific BSC indicators or suggest additional steps or indicators to make the process or business more sustainable (see Table  4 ). Nonetheless, the majority of the papers agree on the need for integrated and multidimensional measurement systems, such as the BSC, and on the importance of directly linking performance measurement to an organization’s strategy. However, while these papers mention the required link with strategy, the prioritization of indicators according to their strategic importance has been studied very little thus far.

Results for RQ2

For RQ2, the sampled papers were reviewed to distinguish papers with performance indicators from papers without performance indicators. A further distinction was made between indicators found with operationalization (i.e., concretization by means of a question or formula) and those without operationalization. We note that for many indicators, no operationalization was available. We discovered that only 30 of the 76 sampled papers contained some type of performance indicator (namely 3, 5, 6, 7, 11, 16, 17, 18, 20, 22, 26, 27, 30, 35, 37, 40, 43, 46, 49, 51, 52, 53, 55, 57, 58, 59, 60, 66, 71, 73). In total, approximately 380 individual indicators were found throughout all the sampled papers (including duplicates), which were combined based on similarities and modified to use more generic terms. This resulted in 87 indicators with operationalization (“Appendix 3 ”) and 48 indicators without operationalization (“Appendix 4 ”).

The 87 indicators with operationalization were then categorized according to the four perspectives of the BSC (i.e., financial, customer, business processes, and “learning and growth”) (Kaplan and Norton 1996 , 2001 ) and the four established dimensions of process performance (i.e., time, cost, quality, and flexibility) (Dumas et al. 2013 ). In particular, based in the identified indicators, we revealed 11 sub-perspectives within the initial BSC perspectives to better emphasize the focus of the indicators and the different target groups (Table  5 ): (1) financial performance for shareholders and top management, (2) customer-related performance, (3) supplier-related performance, (4) society-related performance, (5) general process performance, (6) time-related process performance, (7) cost-related process performance, (8) process performance related to internal quality, (9) flexibility-related process performance, (10) (digital) innovation performance, and (11) employee-related performance.

For reasons of objectivity, the observed performance indicators were assigned to a single perspective starting from recognized frameworks (Kaplan and Norton 1996 , 2001 ; Dumas et al. 2013 ). Bias was further reduced by following the definitions of Table  5 . Furthermore, the authors of this article first classified the indicators individually and then reached consensus to obtain a more objective categorization.

Additional rationale for the identification of 11 performance perspectives is presented in Table  6 , which compares our observations with the perspectives adopted by the most commonly used performance measurement models (see “ Theoretical background ” section). This comparison allows us to highlight similarities and differences with other respected models. In particular, Table  6 shows that we did not observe a dedicated perspective for strategy (EFQM 2010 ) and that we did not differentiate between financial indicators and market indicators (Cross and Lynch 1988 ). Nonetheless, the similarities in Table  6 prevail. For instance, Cross and Lynch ( 1988 ) also acknowledge different process dimensions. Further, Kueng ( 2000 ) and the EFQM ( 2010 ) also differentiate employee performance from innovation performance, and they both add a separate perspective for results related to the entire society.

Figure  10 summarizes the number of performance indicators that we identified in the process literature per observed performance perspective. Not surprisingly, the initial BSC perspective of internal business process performance contains most of the performance indicators: 29 of 87 indicators. However, the other initial BSC perspectives are also covered by a relatively high number of indicators: 16 indicators for both financial performance and customer-related performance and 26 indicators for “learning and growth”. This result confirms the close link between process performance and organizational performance, as mentioned in the introduction.

The number of performance indicators with operationalization per performance perspective

A more detailed comparison of the perspectives provides interesting refinements to the state of the research. More specifically, Fig.  10 shows that five performance perspectives have more than ten indicators in the sample, indicating that academic research focuses more on financial performance for shareholders and top management and performance related to customers, process time, innovation and employees. On the other hand, fewer than five performance indicators were found in the sample for the perspectives related to suppliers, society, process costs and process flexibility, indicating that the literature focuses less on those perspectives. The latter remains largely overlooked by academic research, possibly due to the newly emerging character of these perspectives.

We must, however, note that the majority of the performance indicators are mentioned in only a few papers. For instance, 59 of the 87 indicators were cited in a single paper, whereas the remainder are mentioned in more than one paper. Eleven performance indicators are frequently mentioned in the process literature (i.e., by five or more papers). These indicators include four indicators of customer-related performance (i.e., customer complaints, perceived customer satisfaction, query time, and delivery reliability), three indicators of time-related process performance (i.e., process cycle time, sub-process turnaround time, and process waiting time), one cost-related performance indicator (i.e., process cost), two indicators of process performance related to internal quality (i.e., quality of internal outputs and deadline adherence), and one indicator of employee performance (i.e., perceived employee satisfaction).

Consistent with “ Performance indicators ” section, the different performance perspectives are a combination of financial or cost-related indicators with non-financial data. The latter also take the upper hand in our sample. Furthermore, the sample includes a combination of objective and subjective indicators, and the vast majority are objective indicators. Only eight indicators explicitly refer to qualitative scales; for instance, to measure the degree of satisfaction of the different stakeholder groups. For all the other performance indicators, a quantifiable alternative is provided.

It is important to remember that a distinction was made between the indicators with operationalization and those without operationalization. The list of 87 performance indicators, as given in “Appendix 3 ”, can thus be extended with those indicators for which operationalization is missing in the reviewed literature. Specifically, we found 48 additional performance indicators (“Appendix 4 ”) that mainly address supplier performance, process performance related to costs and flexibility, and the employee-related aspects of digital innovation. Consequently, this structured literature review uncovered a total of 135 performance indicators that are directly or indirectly linked to business process performance.

Finally, the total list of 135 performance indicators was evaluated for its comprehensiveness by comparing the identified indicators with other BSC variants that were not included in our sample. More specifically, based on a random search, we looked for two BSC variants in the Web of Science that did not fit the search strategy of this structured literature review: one that did not fit the search term of “business process*” (Hubbard 2009 ) and another that did not fit any of the performance-related search terms of “performance indicator*”, “performance metric*” or “performance measur*” (Bronzo et al. 2013 ). These two BSC variants cover 30 and 17 performance indicators, respectively, and are thus less comprehensive than the extended list presented in this study. Most of the performance indicators suggested by the two BSC variants are either directly covered in our findings or could be derived after recalculations. Only five performance indicators could not be linked to our list of 135 indicators, and these suggest possible refinements regarding (1) the growth potential of employees, (2) new markets, (3) the social performance of suppliers, (4) philanthropy, or (5) industry-specific events.

This structured literature review culminated in an extended list of 140 performance indicators: 87 indicators with operationalization, 48 indicators without operationalization and 5 refinements derived from two other BSC variants. The evaluation of our findings against two BSC variants validated our work in the sense that we present a more exhaustive list of performance indicators, with operationalization for most, and that only minor refinements could be added. However, the comprehensiveness of our findings can be claimed only to a certain extent given the limitations of our predefined search strategy and the lack of empirical validation by subject-matter experts or organizations. Notwithstanding these limitations, conclusions can be drawn from the large sample of 76 papers to respond to the research questions (RQs).

Regarding RQ1 on the state of the research on business process performance measurement, the literature review provided additional evidence for the omnipresence of the BSC. Most of the sampled papers mentioned or used the BSC as a starting point and basis for their research and analysis. The literature study also showed a variety of research topics, ranging from behavioral-science to design-science research and from a focus on performance measurement models to a focus on performance indicators. In addition to inconsistencies in the terminology used to describe performance indicators and targets, the main weakness uncovered in this literature review deals with the concretization of performance indicators supplementing performance measurement systems. The SLR results suggest that none of the reviewed papers offers a comprehensive measurement framework, specifically one that includes and extends the BSC perspectives, is process-driven and encompasses as many concrete performance indicators as possible. Such a comprehensive framework could be used as a checklist or a best practice for reference when defining specific performance indicators. Hence, the current literature review offers a first step towards such a comprehensive framework by means of an extended list of possible performance indicators bundled in 11 performance perspectives (RQ2).

Regarding RQ2 on process performance indicators, the literature study revealed that scholars measure performance in many different ways and without sharing much detail regarding the operationalization of the measurement instruments, which makes a comparison of research results more difficult. As such, the extended list of performance indicators is our main contribution and fills a gap in the literature by providing a detailed overview of performance indicators mentioned or used in the literature on business process performance. Another novel aspect is that we responded to the criticism of missing perspectives in the original BSC (EFQM 2010 ; Hubbard 2009 ; Kueng 2000 ) and identified the narrow view of performance typically taken in the process literature (Dumas et al. 2013 ). Figures  1 and 2 are now combined and extended in a more exhaustive way, namely by means of more perspectives than are offered by other attempts (Table  6 ), by explicitly differentiating between performance drivers (or lead indicators) and performance outcomes (or lag indicators), and by considering concrete performance indicators.

Our work also demonstrated that all perspectives in the BSC (Kaplan and Norton 1996 , 2001 ) relate to business process performance to some degree. In other words, while the BSC is a strategic tool for organizational performance measurement, it is actually based on indicators that originate from business processes. More specifically, in addition to the perspective of internal business processes, the financial performance perspective typically refers to sales or revenues gained while doing business, particularly after executing business processes. The customer perspective relates to the implications of product or service delivery, specifically to the interactions throughout business processes, whereas the “learning and growth” perspective relates to innovations in the way of working (i.e., business processes) and the degree to which employees are prepared to conduct and innovate business processes. The BSC, however, does not present sub-perspectives and thus takes a more high-level view of performance. Hence, the BSC can be extended based on other categorizations made in the reviewed literature; for instance, related to internal/external, strategic/operational, financial/non-financial, or cost/time/quality/flexibility.

Therefore, this study refined the initial BSC perspectives into eleven performance perspectives (Fig.  11 ) by applying three other performance measurement models (Cross and Lynch 1988 ; EFQM 2010 ; Kueng 2000 ) and the respected Devil’s quadrangle for process performance (Dumas et al. 2013 ). Additionally, a more holistic view of business process performance can be obtained by measuring each performance perspective of Fig.  11 than can be achieved by using the established dimensions of time, cost, quality and flexibility as commonly proposed in the process literature (Dumas et al. 2013 ). As such, this study demonstrated a highly relevant synergy between the disciplines of process management, organization management and performance management.

An overview of the observed performance perspectives in the business process literature

We also found out that not all the performance perspectives in Fig.  11 are equally represented in the studied literature. In particular, the perspectives related to suppliers, society, process costs and process flexibility seem under-researched thus far.

The eleven performance perspectives (Fig.  11 ) can be used by organizations and scholars to measure the performance of business processes in a more holistic way, considering the implications for different target groups. For each perspective, performance indicators can be selected that fit particular needs. Thus, we do not assert that every indicator in the extended list of 140 performance indicators should always be measured, since “ Theoretical background ” section emphasized the need for organization-dependent indicators aligned with an organization’s strategy. Instead, our extended list can be a starting point for finding and using appropriate indicators for each performance perspective, without losing much time reflecting on possible indicators or ways to concretize those indicators. Similarly, the list can be used by scholars, since many studies in both the process literature and management literature intend to measure the performance outcomes of theoretical constructs or developed artifacts.

Consistent with the above, we acknowledge that the observed performance indicators originate from different models and paradigms or can be specific to certain processes or sectors. Since our intention is to provide an exhaustive list of indicators that can be applied to measure business process performance, the indicators are not necessarily fully compatible. Instead, our findings allow the recognition of the role of a business context (i.e., the peculiarities of a business activity, an organization or other circumstances). For instance, a manufacturing organization might choose different indicators from our list than a service or non-profit organization (e.g., manufacturing lead time versus friendliness, or carbon dioxide emission versus stakeholder satisfaction).

Another point of discussion is dedicated to the difference between the performance of specific processes (known as “process performance”) and the performance of the entire process portfolio (also called “BPM performance”). While some indicators in our extended list clearly go beyond a single process (e.g., competence-related indicators or employee absenteeism), it is our opinion that the actual performance of multiple processes can be aggregated to obtain BPM performance (e.g., the sum of process waiting times). This distinction between (actual) process performance and BPM performance is useful; for instance, for supplementing models that try to predict the (expected) performance based on capability development, such as process maturity models (e.g., CMMI) and BPM maturity models (Hammer 2007 ; McCormack and Johnson 2001 ). Nonetheless, since this study has shown a close link between process performance, BPM performance, and organizational performance, it seems better to refer to different performance perspectives than to differentiate between such performance types.

In future research, the comprehensiveness of the extended list of performance indicators can be empirically validated by subject-matter experts. Additionally, case studies can be conducted in which organizations apply the list as a supplement to performance measurement models in order to facilitate the selection of indicators for their specific business context. The least covered perspectives in the academic research also seem to be those that are newly emerging (namely, the perspectives related to close collaboration with suppliers, society/sustainability and process flexibility or agility), and these need more attention in future research. Another research avenue is to elaborate on the notion of a business context; for instance, by investigating what it means to have a strategic fit (Venkatraman 1989 ) in terms of performance measurement and which strategies (Miller and Friesen 1986 ; Porter 2008 ; Treacy and Wiersema 1993 ) are typically associated with which performance indicators. Additionally, the impact of environmental aspects, such as market velocity (Eisenhardt and Martin 2000 ), on the choice of performance indicators can be taken into account in future research.

Business quotes such as “If you cannot measure it, you cannot manage it” or “What is measured improves” (P. Drucker) are sometimes criticized because not all important things seem measurable (Ryan 2014 ). Nonetheless, given the perceived need of managers to measure their business and the wide variety of performance indicators (i.e., ranging from quantitative to qualitative and from financial to non-financial), this structured literature review has presented the status of the research on business process performance measurement. This structured approach allowed us to detect weaknesses or inadequacies in the current literature, particularly regarding the definition and concretization of possible performance indicators. We continued by taking a holistic view of the categorization of the observed performance indicators (i.e., measures or metrics) into 11 performance perspectives based on relevant performance measurement models and established process performance dimensions.

The identified performance indicators within the 11 perspectives constitute an extended list from which practitioners and researchers can select appropriate indicators depending on their needs. In total, the structured literature review resulted in 140 possible performance indicators: 87 indicators with operationalization, 48 additional indicators that need further concretization, and 5 refinements based on other Balanced Scorecard (BSC) variants. As such, the 11 performance perspectives with related indicators can be considered a conceptual framework that was derived from the current process literature and theoretically validated by established measurement approaches in organization management.

Future research can empirically validate the conceptual framework by involving subject-matter experts to assess the comprehensiveness of the extended list and refine the missing concretizations, and by undertaking case studies in which the extended list can be applied by specific organizations. Other research avenues exist to investigate the link between actual process performance and expected process performance (as measured in maturity models) or the impact of certain strategic or environmental aspects on the choice of specific performance indicators. Such findings are needed to supplement and enrich existing performance measurement systems.

Abbreviations

behavioral science

business process management

balanced scorecard

design-science

research question

structured literature review

keyword in topic

keyword in title

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Authors’ contributions

AVL initiated the conception and design of the study, while AS was responsible for the collection of data (sampling) and identification of performance indicators. The analysis and interpretation of the data was conducted by both authors. AVL was involved in drafting and coordinating the manuscript, and AS in reviewing it critically. Both authors read and approved the final manuscript.

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See Table  7 .

Appendix 2: The mapping of the structured literature review

The mapping details per sampled paper can be found here.

https://drive.google.com/file/d/0B_2VpjwsRLrlRHhfRHJ4ZFBWdEE/view?usp=sharing .

See Table  8 .

See Table  9 .

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Van Looy, A., Shafagatova, A. Business process performance measurement: a structured literature review of indicators, measures and metrics. SpringerPlus 5 , 1797 (2016). https://doi.org/10.1186/s40064-016-3498-1

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• Business process
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Effective application of process improvement patterns to business processes

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Improving the operational effectiveness and efficiency of processes is a fundamental task of business process management (BPM). There exist many proposals of process improvement patterns (PIPs) as practices that aim at supporting this goal. Selecting and implementing relevant PIPs are therefore an important prerequisite for establishing process-aware information systems in enterprises. Nevertheless, there is still a gap regarding the validation of PIPs with respect to their actual business value for a specific application scenario before implementation investments are incurred. Based on empirical research as well as experiences from BPM projects, this paper proposes a method to tackle this challenge. Our approach toward the assessment of process improvement patterns considers real-world constraints such as the role of senior stakeholders or the cost of adapting available IT systems. In addition, it outlines process improvement potentials that arise from the information technology infrastructure available to organizations, particularly regarding the combination of enterprise resource planning with business process intelligence. Our approach is illustrated along a real-world business process from human resource management. The latter covers a transactional volume of about 29,000 process instances over a period of 1 year. Overall, our approach enables both practitioners and researchers to reasonably assess PIPs before taking any process implementation decision.

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1 Introduction

Research on business process management (BPM) and process-aware information systems (PAISs) has resulted in many contributions that discuss options to improve the quality, performance, and economic viability of business processes [ 1 ]. Examples range from individual “best practices” [ 2 ] to comprehensive business process quality frameworks [ 3 , 4 ]. In this context, we refer to process improvement patterns (PIPs) as generic concepts for enhancing particular aspects of business processes. As an example, consider decision processes that require to appraise various decision criteria. The respective appraisal tasks can be arranged to reach a decision with as little effort and as quickly as possible. This can be achieved by executing tasks with a high probability of providing sufficient information for a decision and with comparably low execution effort earlier in the process. This principle is known as “knockout” [ 5 ]. It constitutes a first example of a process improvement pattern.

(Knockout principle) Consider a process for handling invoices received from suppliers. To determine whether the invoice should be paid, we want to check whether it is in line with purchase order data. In addition, we need to ensure that there is a sign-off from the responsible manager. The former check can be fully automated in the context of ERP systems and therefore be executed with little effort. Thus, it makes sense to execute this check first and possibly “knock out” the invoice before incurring the much greater effort of (manual) sign-off.

1.1 Research challenges

To ensure practical relevance, the actual business value of PIPs needs to be demonstrated to practitioners, thus enabling reasonable implementation decisions. In the context of this issue, there exist many propositions for empirically establishing the effectiveness of PIPs. These include anecdotal evidence [ 6 ], case studies [ 7 ], and surveys [ 8 ]. Commonly, these approaches are based on ex-post (i.e., hindsight) appraisal of qualitative evidence given by process managers or other stakeholders to obtain general insights applicable to comparable cases.

However, there still exists a gap regarding the a priori (i.e., in advance) assessment of PIPs considering a particular application scenario , which may range from an organization’s strategy and goals to its existing business process and information systems landscape. In particular, this gap should be bridged for the following reasons:

Similar to design patterns in software engineering [ 9 ], PIPs constitute abstract concepts that may or may not be useful in a particular context. Experience from other scenarios, which may widely differ from the one at hand, is thus not sufficient to take reasonable decisions on the implementation of organizational changes or process-aware information systems (PAISs).

Ex-post evidence is usually obtained from persons involved in the respective implementation projects. In turn, this leads to a source of bias. Moreover, a priori assessment allows addressing a far wider spectrum of PIPs. In particular, it is not necessary to complete implementation projects before a PIP can be assessed.

Combining PAISs with process intelligence tools [ 10 – 12 ] opens up new opportunities to quantitatively and qualitatively gauge real-world business processes. This should be leveraged for scenario-specific PIP assessment.

Effective PAIS development requires to consider process improvement potentials before any implementation effort is incurred. Accordingly, PAIS development should start with a requirements definition which, in turn, is based on adequate process design considering relevant PIPs.

To enable a priori PIP assessment, this paper tackles the following challenges:

Challenge 1 Describe an approach toward a priori PIP assessment reflecting and summarizing common practice in the field.

Challenge 2 Evaluate the approach by applying it to a substantial real-world case.

Challenge 3 Reconcile the approach to scientific standards by applying guidelines for empirical research in information systems (IS).

1.2 Contribution

This article constitutes an amended version of the work we presented in [ 13 ]. Reflecting the considerations made above, [ 13 ] provides an approach toward a priori assessment of PIPs. In particular, the contribution of [ 13 ] is based on the following aspects:

The proposal we presented in [ 13 ] provides a standard approach to evaluate the impact of PIPs on organizational objectives for specific application scenarios. Thus, it supports well-founded decisions on the implementation of corresponding adaptations of business processes. Moreover, it contributes to bridge the gap between generic PIPs proposed by the BPM research community and real-world application scenarios. This way, it enhances the practical relevance of proposed PIPs.

It considers scientific rigor by applying an appropriate research framework.

It reflects practical requirements, which could be demonstrated through an experience report covering a substantial real-world business process.

In particular, when discussing the validity of our research design, execution, and results with practitioners, we made a number of observations that are generally applicable to process improvement projects based on PIPs. Since these may be helpful for practical application as well as future research, they are included as project recommendations in this paper.

This article complements our previous results with the following additional contributions:

It extends the presentation of the sample case used for our experience report with empirical results based on process mining [ 11 ]. Thus, it illustrates the application of this technology to a practical case.

It provides a discussion of open challenges regarding process improvement tools.

It provides a more profound discussion of the applied process improvement methodology, thus rendering the approach more accessible for application to other scenarios.

It discusses the complete set of process improvement measures that resulted from the application of PIPs to the sample case.

It describes the results obtained when revisiting our process improvement measures 14 months after having completed the initial process improvement project.

It provides a substantially extended discussion of related work.

It comprises a more detailed reflection of our results against Challenges 1–3, as well as an assessment of the general applicability of our approach. This includes a discussion of relevant limitations and strategies to address these.

The remainder of this article is structured as follows: Sect.  2 describes the sample process we use to illustrate our approach. Section  3 presents our approach toward PIP assessment. In the sense of an experience report, Sect.  4 describes the results obtained when applying the approach to the sample process from Sect.  2 . Section  5 discusses the state of the art in PIP assessment as well as other related work. Finally, Sects.  6 and 7 evaluate our results referring to the challenges discussed above and conclude the paper.

2 Sample case: applications management process

The business process we use to illustrate the concepts presented in this paper stems from the field of human resource management. It addresses the handling of incoming job applications to fill open positions in a professional services firm. Figure  1 describes the business objective of this process according to a notation we developed in [ 14 ]. The objective of the process is to achieve one of two states for each job application: Either the application is refused, or a job offer is sent to the applicant. A job offer shall be sent if the following conditions are met: (1) The application documents have been accepted in terms of quality (e.g., with regard to the CV), (2) an interview has taken place with a positive feedback, (3) basic conditions have been agreed on between both parties, and (4) senior management approval has been obtained. If one of these requirements is not met, a letter of refusal has to be sent.

Sample business objective: handling incoming applications

Based on our discussions with stakeholders and the results of process mining, we can model the business process implementing this business objective. For this purpose, we use BPMN (cf. Fig.  2 , [ 15 ]). For the sake of brevity, we slightly simplify the model and omit a detailed description of its elements. As an example of the relation between the business objective and the process model, consider the conditions the business objective poses toward sending a job offer. The process model transforms these conditions into respective checking activities (e.g., Technical quality into Check documents ) and XOR decision gateways. Note that there is not necessarily a one-on-one relation between conditions and checking activities. Further, there may be multiple process implementation alternatives for a given business objective (e.g., multiple conditions may be checked within one activity).

Sample process: handling incoming applications (BPMN notation)

Figure  3 breaks down the total number of applications handled in a time period of one fiscal year into the number of applications for each possible termination state of the process. Note that the termination states from Fig.  3 correspond to potential paths through the process model from Fig.  2 . We will refer to this overview when discussing our research execution in Sect.  4 . We obtained a corresponding data sample of 27,205 process instances from the log database tables of the PAIS supporting the business process (in this case, an SAP ERP system). Each process instance covers one application. Thus, 1,972 out of the 29,177 applications of Fig.  3 are not included in the data sample. These comprise, for example, applications handled in the business units without involvement in the human resource (HR) function. These applications are not traceable in the PAIS.

Termination states of the application process: one fiscal year sample

Figure  4 shows a process map generated with the Disco process mining tool [ 16 ] when applying it to the data sample. Footnote 1 For the sake of readability, this process map has been filtered to solely comprise enactment traces that occur frequently and events that are relevant for our analyses.

Filtered process map: one fiscal year data sample

The process map is an example of the results that can be generated with process mining tools. In the following, we will use process mining and other techniques to analyze our log data sample with respect to process improvement potentials. The process map should be considered as an amendment, but not as a replacement of “traditional” process models such as the one presented in Fig.  2 :

The process map is based on events logged in the PAIS. Not all events directly reflect a corresponding activity in the process model, and identifiers of events might differ from the ones of corresponding activities. There may be activities not reflected in a logged event or events not triggered by an activity from the process model.

The process map shows the actual frequency of events in the data sample. Thus, it reflects as-is process execution, which may differ from to-be process design as recorded in the process model.

The process map needs to be interpreted with the support of experienced stakeholders. In our sample case, for example, application refusal events are used to purge the database of received applications to comply with privacy regulations. Further, not all hirings are handled through the corresponding end events. Issues like these need to be understood when interpreting the process map. However, this understanding is useful for process improvement as well.

3 Methodology

Like other IS artifacts, PIPs constitute goal-bound artificial constructs in the sense of the design science paradigm [ 20 ] to be evaluated in terms of “value or utility” [ 21 ]. In our context, this results in a particular challenge. While PIPs are abstract concepts applicable to a broad range of scenarios, their business value must be determined considering the specific use case to enable a decision whether the PIP should be implemented. To this end, we use an extended conceptual framework as summarized in Fig.  5 .

Extended conceptual framework

Beyond the concepts of PIPs and business processes or application scenarios, we introduce organizational objectives, process improvement objectives, and process improvement measures:

Organizational objectives reflect strategic goals an organization wants to achieve with respect to an application scenario . Examples of organizational objectives that apply to many scenarios include the effectiveness of process output, cost savings, or compliance with regulations [ 1 , 22 ]. Note that these examples can be used as a starting point to identify organizational objectives relevant to a particular application scenario. In principle, such objectives are generic, but how they are prioritized against each other is specific to an organization’s strategy.

Process improvement objectives (PIOs) comprise characteristics that enhance a process considering organizational objectives. PIOs can be viewed as a refinement of organizational objectives considering the particular challenges associated with a concrete application scenario. In a step-by-step approach, PIOs can be refined into a tree structure, as will be exemplified when discussing our application scenario in Sect.  4 . The resulting top-down model is a useful mental technique to ensure a comprehensive perspective on process improvement. Note that similar considerations are used in goal-oriented requirements engineering (cf. Sect.  5.3 ) and value-based management [ 23 ]. This procedure can be aborted as soon as the resulting PIOs are sufficiently granular to allow for the application of PIPs. PIOs thus constitute the “bridge” between abstract organizational objectives and concrete PIPs. The relevance of PIOs to organizational objectives may be evident, or it may require additional validation. As an example of immediately evident PIOs, consider the elimination of obviously redundant tasks to reduce costs. As an example of PIOs that require validation, consider short cycle times. It is not necessarily a strategic goal to enact processes as fast as possible. However, this may be a PIO if a link between cycle times and a particular organizational objective (e.g., reducing costs ) can be demonstrated. PIOs thus provide an additional layer of abstraction as a “shortcut” between improvement measures and organizational objectives. For the above example, potential improvement measures might be validated by demonstrating a positive impact on cycle times instead of overall cost. PIOs can also be viewed as a tool to identify PIPs relevant for the application scenario: Available PIPs are considered with regard to whether they can contribute to a PIO. For example, the parallel execution of formerly sequential tasks constitutes a PIP that may contribute to shorter cycle times as an exemplary PIO. Note that the concept of PIOs corresponds to the identification of stakeholders’ goals, which has been proposed as a requirement for empirical IS research in [ 24 ].

Process improvement measures (PIMs) are bundles of actions considered for joint implementation. Footnote 2 They reflect the application of process improvement patterns (PIPs) to a specific process in order to realize PIOs. Several PIPs may be bundled into one PIM for joint implementation, depending on the given application scenario. As an example of a PIM, consider the implementation of a new workflow tool, which may incorporate multiple abstract PIPs. A PIM thus applies one or more PIPs to a specific business process to address one or more particular PIOs. Assessing PIPs for a particular application scenario thus amounts to the assessment of the business value of corresponding PIMs considering relevant PIOs.

Note that, considering the arrows, Fig.  5 may also be read as a top-down method for process improvement. Section  4 further describes its application: General organizational objectives are refined to PIOs specific to the considered business process or application scenario. Then, PIPs relevant to the concrete scenario are selected from a generic set of generally available PIPs and bundled into concise PIMs. Specifically to the application scenario, PIMs are described in sufficient detail to enable to discuss and decide on their implementation.

Business processes and PIMs, as our unit of study, are implemented by means of PAISs. To maintain scientific rigor, their assessment should take into account requirements known from the empirical evaluation of propositions in software engineering or IS research. In [ 24 ], the authors subsume requirements in terms of scientific methodology for evaluation approaches in IS research. Figure  6 provides an overview on the basic concepts described there. In the following, we align our approach to [ 24 ]. We describe how each component is reflected in our proposition. Note that the (general) statements made should be further refined for each application scenario. From a practical perspective, this will ensure a common understanding by all project participants. Thus, respective considerations are included in the following paragraphs as well.

Problem statement and research design: required components

3.1 Problem statement

The first four components we address constitute the problem statement according to [ 24 ].

Research question (“What do we want to know?”) Should PIMs be implemented to better meet organizational objectives? Note that this research question refers to PIMs instead of PIPs in order to reflect our goal of scenario-specific assessment.

For our sample case, the research question can be refined to the question whether PIMs should be implemented to reduce cost per hire (cf. Sect.  4.1 ).

Unit of study (“About what?”) The business process to be improved and the proposed PIMs comprising PIPs constitute our unit of study. Effectively selecting PIPs and bundling them into scenario-specific PIMs require the participation of knowledgeable, but also creative project members. For example, the participants of workshops to discuss PIMs should be carefully selected. In this regard, researchers may contribute a valuable “outside-in view” based on, for example, experience from other scenarios.

Regarding our sample case, the application management process and the proposed PIMs as the unit of study are described in detail in Sects.  2 and 4.3 , respectively.

Relevant concepts (“What do we know in advance?”) Related work to be considered generally includes conceptual work on PIPs, case studies on comparable processes, and benchmarks available for the application scenario. In this regard, it is helpful to ensure proper research of available literature as well as a thorough use of available organizational knowledge (e.g., through selection of appropriate interview partners).

For our exemplary application scenario, we use a framework of process redesign practices [ 2 ], our own research into PIPs, a cost per hire benchmark, and available research on “knockout” processes [ 5 ].

Research goal (“Why do we want to know?”) Implementing PIMs will result in cost and risks incurred (e.g., process disruptions). To avoid unnecessary cost and risks, implementation decisions should be based on appropriate investigation of whether implementing PIMs will enable to better meet organizational objectives. Implementation decisions should consider not only benefits in day-to-day process operations, but also required investments and future operating cost or total cost of ownership.

3.2 Research design

The five components described in the following constitute the research design of our approach in terms of data collection , measurement , and data analysis .

Unit of data collection To understand the application scenario, we require an as-is process description to reflect process design, a process instances sample to reflect process execution, and PIOs to reflect refined organizational objectives. Depending on the application scenario and practical considerations, the process instances sample can be given as a PAIS data extract, as a set of interviews with involved people, as a set of cases directly observed, or as a combination thereof. To assess PIPs, we require descriptions of available PIPs and scenario-specific propositions of PIMs. Note that data collection should cover both process design and actual process execution. This way, PIOs can be identified prospectively (based on the process design) and retrospectively (based on the process execution). Immediate observations are preferable to indirectly related process information. Depending on the application scenario and practical considerations, the process instances sample can be given as a PAIS data extract, as a set of interviews with involved people, as a set of cases directly observed, or as a combination thereof.

Regarding our sample application scenario, we could refer to a business objective model and a flowchart of the process, a statistic on the results of process execution, and a substantial PAIS execution data extract (cf. Sect.  2 ). To assess PIPs, we use PIP descriptions available in the literature and from our own research, and PIMs as described below (cf. Sect.  4.3 ).

Environment of data collection Our proposition primarily aims at improving existing business processes. Hence, data are collected in the field to reflect the actual situation as best as possible. The environment of data collection thus generally comprises process stakeholders (i.e., contact partners involved in process execution, recipients of process output, or suppliers of process input) as well as relevant documentation and PAISs. The environment of data collection should be as broad as practically reasonable in order to facilitate identifying all PIOs that are relevant to organizational objectives, and to enable appropriate assessment of PIPs and PIMs.

Regarding our sample case, the environment of data collection comprised the head of recruiting, a business unit HR partner, business unit team managers, the PAIS administrator, and recruiting team members as process stakeholders. In terms of documentation, we used regular recruiting management reports and PAIS status codes. The PAIS used to support the business process was available as well. As a limitation to our sample environment of data collection, applicants as a group of process stakeholders were not represented in the environment of data collection due to practical requirements. Because of privacy regulations, applicants’ contact data may only be used to process the application, but not for other purposes.

Measurement instruments Our approach is based on elaborating PIOs and PIMs in a step-by-step approach. Draft PIOs and PIMs are thus used to document input from the environment of data collection, and constitute measurement instruments comparable to semi-structured questionnaires. These are amended with the proceedings documentation from interviews and workshops (see measurement procedures below). In addition, depending on the process instances sample, process execution tracing capabilities in PAISs or statistical process control (SPC) procedures also need to be considered. Note that measurement instruments should consider usability criteria with regard to stakeholders involved in measurement procedures. For example, this requires using terms customary to the organization when phrasing PIOs and PIMs.

Regarding our sample application scenario, PIOs and PIMs used as measurement instruments are described in Sects.  4.2 and 4.3 , respectively. In addition, we used workshop proceedings, confirmation letters on results reconciliation (via email), and procedures to extract execution data from the PAIS used to manage incoming applications.

Measurement procedures Depending on the application scenario and practical considerations, relevant measurement procedures comprise stakeholder interviews, stakeholder workshops, and questionnaire procedures. Process mining can be used if the sample of process instances is based on a PAIS data extract. Measurement procedures should take into account customary practices of the organization, e.g., by using standard templates for meeting proceedings.

On-site measurement procedures (i.e., observing the process in its operations environment) can help to identify additional PIOs to be addressed for process improvement by giving a clearer picture of day-to-day process issues.

Regarding our sample case, we used telephone and face-to-face interviews with follow-up reconciliation of proceedings, a recruitment center site visit, and process mining with Disco.

Data analysis procedures In general, relevant data analysis procedures include qualitative analysis of workshop and interview results and quantitative analyses of process instance samples depending on the measurement instruments applied. Note that data analysis procedures need to be flexibly adapted to the step-by-step refinement of PIOs and PIMs and to the form of quantitative data available on the process instances sample. In practice, this may lead to a mix of tools actually applied. In this context, for example, statistical analysis tools can significantly reduce quantitative analysis effort and therefore enable enhancing the search scope for relevant PIOs.

Regarding our sample case, a qualitative analysis was conducted together with stakeholders as described in Sect.  4.3 . In turn, the quantitative analysis comprised filtering of sub-process views in a process mining tool (Disco), re-extraction of filtered samples and import into a spreadsheet application, conversion of the event log into a “case log” (i.e., an array of events for each process instance), computation of cycle time attributes for each case, and statistical analysis with Minitab.

4 Sample case: process improvement patterns assessment

We now apply our extended conceptual framework comprising organizational objectives, PIOs, PIPs, and PIMs (cf. Fig.  5 ) as well as our research design to our sample application scenario. Further, we summarize our observations regarding the use of tools and systems for empirically analyzing our sample process, which may be useful for further developments in this regard.

4.1 Organizational objectives

As discussed, obtaining clarity about the content and business value of organizational objectives constitutes a fundamental prerequisite to ensure the relevance of PIP assessment. In our sample application field (i.e., recruiting), organizations strive to fill vacant positions quickly, cost-effectively, and with suitable candidates. To achieve these goals, personnel marketing is responsible to generate a sufficient number of suitable applications, while the purpose of our sample process (i.e., managing job applications) is to convert applications into actual hires.

Thus, organizational objectives for the sample application scenario include reducing the time needed until open positions are filled , reducing cost per hire , and improving the quality of hired applicants . Out of this set of objectives, reducing cost per hire is well suited for illustrating our approach. In particular, the issue of cost is transferable to many other scenarios. More precisely, the following considerations apply for our sample process:

Reducing cost per hire as organizational objective The cost per hire key performance indicator captures the total cost of both personnel marketing and applications management. While recruiting cost spent per application is proprietary data, based on experiences from projects with clients we assume an amount of about 400 Euros. In our sample scenario, generating and managing about 29,000 applications per year would thus result in 11.6m Euros total cost, with cost per hire at around 4,000 Euros. Since hiring cost for talent in professional services will be higher than in, for example, manufacturing, this value corresponds well to the average of 4,285 USD reported as cost per hire for larger organizations by a benchmarking organization [ 25 ]. Further, it seems rather conservative considering that professional recruiting consultants commonly charge half a year’s salary for successful hires, depending on industry. This calculation demonstrates the high relevance of reducing cost per hire through an improved application handling process.

Note that while reducing cost per hire has been chosen to illustrate our approach, the other objectives remain highly relevant. In particular, they need to be kept in mind when designing PIMs to avoid improving the process toward one objective at the expense of others. As an example, improving recruitment cost should not result in eliminating face-to-face interviews with candidates since this would probably reduce cost at the expense of the quality of applicants hired.

4.2 Process improvement objectives (PIOs)

PIOs pertain to characteristics of the business process that affect the organizational objectives we want to improve on. They serve as a “shortcut” to facilitate discussing the business value of PIMs without reverting to high-level organizational objectives. In our sample case, we refine the organizational objective to reduce cost per hire in a step-by-step approach by asking the question what drives cost per hire or, subsequently, the resulting lower-level PIOs.

Figure  7 presents relevant aspects of the resulting tree structure: Initially, total cost per hire is driven by the cost associated with each process instance (i.e., with each individual application), and, since it is possible that multiple process instances are needed to fill one position, with the overall number of process instances required. Both aspects may be optimized to reduce cost per hire, but are still rather abstract and will not allow applying PIPs without further refinement.

Deriving process improvement objectives

On the one hand, cost per process instance is determined by the cost of production factors (e.g., the cost of employees’ working time or the cost of IT systems used) and the amount of effort spent on each process instance. Both drivers will occur in any tree of PIOs dealing with cost reduction. Factor costs, however, are generally unsuitable as a PIO since they are not governed by process designers and managers. Therefore, they cannot be subjected to process improvement efforts. Rather, they should be considered as a factor given externally which may affect assessment results. As an example, consider the impact of location decisions on labor costs.

(The impact of factor costs on PIP evaluation) Particularly in large organizations, it is a common practice to bundle administrative business processes into “shared services organizations” [ 26 ]. In this context, labor costs constitute an important factor when deciding on the location of the shared services organization. In turn, this decision impacts considerations on the economic viability of process improvement measures. For example, when considering capital investments to automate manual activities, like matching incoming payments on bank statements against invoices issued to customers, lower labor costs will increase the payback time of the investment, thus rendering its implementation less attractive.

On the other hand, cost per process instance is determined by the quantity of production factors associated with each process instance. In our sample process, factors besides manual labor can be neglected, as will be the case for most administrative business processes. Accordingly, reducing effort per process instance pertains to reducing manual processing effort . This PIO lies at the core of many PIPs commonly used in practice, such as task elimination , task automation , or knockout [ 2 ], and has thus reached a sufficient degree of refinement.

Besides reducing cost per process instance , cost per hire might be improved by reducing the number of process instances required over time. This option corresponds to the elimination of in-efficacious process instances that do not terminate in a desirable state according to the underlying business objective. It closely resembles methods applied in common quality management approaches that aim at reducing “causes of poor quality” [ 27 ]. In particular, every in-efficacious process instance can be viewed as a quality issue in the business process. Note that the overall effect of quality management on cost and firm performance has been well recognized and empirically demonstrated [ 28 ]. This option is particularly interesting since associated measures can often be implemented with limited investments. Hence, further considerations on our sample case will focus on this PIO.

In our sample case, cost per hire is driven by the general efficiency of the application management process, but also by the frequency of process instances terminating in one of the states marked as “critical” in Fig.  3 . The following considerations apply in this regard:

Not approving a job offer after a successful interview may be caused by defective steering of capacities (i.e., job vacancies), defective communication of terms to be offered, or defective review of application documents.

Job offers declined by applicants mostly means that the applicant does not approve of conditions offered, did not have a good impression during the application process, or has decided to take another job offer.

Since terminating the process in these states means that significant effort has been incurred while still failing to hire a promising candidate, organizational objectives are clearly violated: On average, only one out of six applications will successfully pass interviews. However, considering critical cases with defective termination events (cf. Fig.  3 ), only one out of ten applicants can be hired. In other words, if the process enactment defects lined out could be fully eliminated, only about 18,000 applications would have to be acquired and managed to cover demand. This would reduce total hiring cost by about 4.6m Euros.

Based on the considerations made above, we focus on PIOs to reduce effort per process instance or to reduce the probability of the defective process outcomes described. Table  1 summarizes the resulting topics.

Note that for the first PIO (i.e., Reduce manual processing effort ) there is an evident link to our organizational objective of reducing cost per hire . However, the second and third PIOs (i.e., Reduce failed approvals and Reduce cycle times ) are based on hypotheses on what can be done to reduce process enactment defects affecting the organizational objective. Accordingly, they require qualitative or quantitative evidence to corroborate their relevance for reducing defects and thus improving cost per hire.

For the second PIO (i.e., Reduce failed approvals ), we obtained qualitative evidence by interviewing responsible managers, which confirmed the topics described in Table  1 . Since the reasons for failed approvals are not captured in the PAIS used to manage the application process, quantitative evidence is not available.

For the third PIO (i.e., Reduce cycle times ), the causal link to its underlying defect of applications withdrawn by candidates is not as obvious. Further quantitative analysis is thus required. Figure  8 summarizes the duration between interviews and job offers for the subsets of applicants accepting and declining their offer in a boxplot (this part of total cycle time will later be the subject of process improvement, cf. Sect.  4.3 ). In the figure, the differences between subsets regarding quartiles, median, and mean values appear as relatively small. However, a correlation between cycle times and the probability of a candidate to accept or decline a job offer can be statistically demonstrated

Boxplot Duration interview to job offer in weeks vs. acceptance of job offers

Correlation between job offers declined and cycle times We want to determine whether there is a significant influence of cycle time between application receipt and job offer in weeks on the probability of an applicant accepting or declining a job offer. Accordingly, we use a binary logistic regression test to evaluate the influence of a metric independent variable on a binary dependent variable. For this test, we use a sample of 2,721 job offers representing about 70 % of the annual volume (cf. Fig.  3 ) and consisting of instances fully covered in the PAIS (not all interviews and feedbacks are documented in the PAIS). The sample contains 261 cases where the job offer was eventually declined by the applicant. This is the latest point in the process where withdrawal by the applicant is possible, and a significant amount of effort will have been spent on each respective case. The two samples are independent from each other and have a size of more than 100 cases each. Thus, the binary logistic regression can be applied.

Figure  9 shows an excerpt from the output of the statistical software package we used (Minitab). The p-value of less than 0.05 indicates sufficient evidence to assume a significant correlation between the occurrence of withdrawal and cycle time. Regarding the question whether this correlation can be interpreted as a causal link of cycle times impacting the probability of withdrawal, the following considerations apply:

Cycle time is measured between receipt of the application and the ultimate feedback to the candidate, whereas the withdrawal sample refers to candidates that declined a job offer thereafter . An impact of the occurrence of a withdrawal on cycle time can therefore be ruled out.

There is a plausible explanation for longer cycle times causing withdrawals: It is possible that candidates find another job while waiting for feedback after an interview. This explanation is substantiated by data on withdrawal reasons collected for a sample of 51 withdrawals between October 2013 and January 2014 for one business unit. The sample covers only cases where a reason was given for the withdrawal. In 33 out of 51 cases, the reason cited was a job offer by a third party, and we may assume that longer cycle times provide more opportunities for candidates to find alternative employment.

We discussed potential additional independent variables with a positive effect on both cycle times and the probability of withdrawal with HR management and obtained no evidence on such variables. HR managers even mentioned that particularly sought-after candidates, who can be expected to quickly obtain alternative job offers, are handled with higher priority by business units. This effect might even “hide” part of the correlation between cycle times and probability of withdrawal. However, quantitative evidence on this issue is not available.

According to the “odds ratio” column, a 1-week delay can thus be expected to increase the probability of an applicant declining a job offer by 16 %.

Minitab output excerpt: binary regression test

The significant correlation between cycle times and the probability of withdrawal did not become obvious when just considering median and mean values, but only when executing the binary logic regression test. This observation stresses the necessity to use both sufficient sample sizes and appropriate statistical methods when dealing with empirical data on business process enactment.

4.3 Process improvement measures (PIMs)

PIPs relevant to our application scenario have been selected by considering a “longlist” of known PIPs in terms of potential contributions to the PIOs described above. In our case, the “longlist” comprised PIPs from a framework by Reijers and Limam Mansar on process redesign practices [ 2 ] (these are marked with an asterisk “*”) as well as from our ongoing research on improving business process quality [ 4 , 29 ]. However, organizations are not limited with regard to the sources of PIPs that can be used. PIOs are thus used as a mental technique to guide the identification of patterns that are useful for the organization. Relevant PIPs are then bundled into PIMs specific to the application scenario.

Table  2 summarizes PIOs and corresponding PIMs as bundles of PIPs.

In actual design and implementation projects, it is common to document and track individual PIMs through measure cards . In the following, we describe the PIMs from Table  2 in more detail using this metaphor. For each PIM, we give a short content description—with PIPs involved marked as italic —and additional details on the following issues:

Implementation describes steps required to realize the measure.

Business value appraises the expected implications considering the impact on PIOs as well as implementation effort.

Stakeholder verification describes the results of validating the PIM through interviews with respective stakeholders.

Note that the PIMs presented here are, by definition, specific to our application scenario. However, their structure as well as the underlying methodology is generally applicable. In terms of content, they exemplify issues commonly found in business process improvement projects, such as the evaluation of IT implementation effort. Moreover, beyond the scope presented here, actual measure cards comprise additional information relevant to project management. This includes project planning, project organization, key milestones with “traffic light” status, risks, next steps, and decision requirements. Reporting on measure cards usually takes place in steering committee meetings of senior management.

PIM Card 2 addresses the reduction in failed approvals as one of the critical cases identified in Fig.  3 .

The abbreviated measure cards presented above exemplify how PIM implementation benefits can be projected and matched against expected implementation effort. However, beyond this quantitative reasoning, qualitative (or “political”) topics may play a role in taking implementation decisions as well, as will be exemplified with PIM Cards 3 and 4.

Changes to the sample process when implementing PIM 3

PIM Card 4 addresses the Reduce cycle times PIO by dealing with one of the underlying drivers for unnecessarily long cycle times.

The final PIM we identified exemplifies an issue that occurs regularly in a top-down approach as employed in our case: Since it is possible that similar PIPs can be used to address various PIOs, overlaps in PIMs content may emerge. This topic needs to be considered in implementation planning and when consolidating recommended PIMs into a “management summary” view (e.g., in terms of overall implementation cost and cost savings potentials). In general, it is preferable to consolidate corresponding PIMs into one overall PIM addressing multiple PIOs. However, even in this case, the top-down approach facilitates obtaining a clear overall picture of potentials to be realized by PIP implementation.

Figure  11 compares PIMs 1–4 in terms of one-off implementation cost and recurring savings potential per year (i.e., gross cost reduction minus additional operating effort, PIM 5 could not be quantified in this respect). Note that the presented PIMs exhibit a fairly positive business case, with ratios between implementation cost and total annual savings below 1 year, and that the most positive business cases can be realized by implementing organizational measures without expensive IT implementation. They constitute good examples of a phenomenon often encountered in practice: in many cases, it is interesting to first identify and resolve existing process defects within the framework of available technology before additional process automation is implemented at huge cost. Once these “quick win” potentials have been leveraged, further process automation should be considered.

Comparison of PIMs

4.4 Implementation results

The process improvement project facilitating our sample case has been concluded in early 2013 with the go-live of the newly implemented PAIS. This section briefly revisits the PIMs discussed above with regard to the results actually achieved. Statements are based on follow-up interviews conducted with stakeholders in March 2014, i.e., about 1 year after go-live. Our interview partners included the head of recruiting operations, the application management process manager, the HR partner of a business unit, and two business unit team managers involved in recruiting (e.g., as interviewers of applicants). To structure the interviews, we used the available PIM cards and collected feedback on their implementation and corresponding results.

PIM Card 1 (application management automation) Discussing this measure with stakeholders during our analysis phase resulted in postponement of the implementation decision because of the required restructuring of job ads. By now, it has been decided to implement the PIM with slight changes as discussed in the following. This decision has been taken because the demonstration of business value documented in PIM Card 1 has led to increased management attention regarding the avoidable manual effort spent on routing applications. The organization is currently undergoing an effort to significantly reduce variability in job ads. In the future, each job ad will have exactly one contact partner from a business unit assigned who will automatically receive screened applications. If the contact partner wants to pass the application to her colleagues for an interview, she will choose the appropriate person from a contact partner data base. This way, manual routing of applications can be largely eliminated.

PIM Card 2 (utilization and capacity management) The issue of utilization and capacity management has been referred to an entirely new “workforce management system” currently under development. This system will interface with the application management PAIS to avoid the issue of routing applications to teams with limited utilization. Note that this functionality will build on the implementation of PIM 1 as discussed above by controlling proposed contact partners from the contact partners database.

PIM Card 3 (standardized terms and conditions) Terms and conditions offered to candidates have been reconciled with an HR consultancy. This assessment has led to the result that current terms and conditions are in line with applicable benchmark values. On that basis, a new policy has been issued that requires deviating terms to be reconciled with business unit management. According to our interview partners, this policy has reduced corresponding cases to a minimum, which has led to a significant reduction in “late refusals” as proposed in the PIM.

PIM Card 4 (managing interview feedback cycle times for successful applicants) According to application management reporting, interview feedback cycle times could be reduced to an average value of 1.4 weeks based on implementing this PIM. Concurrently, the share of “late withdrawals” could be reduced to 7.7 % for the timespan of October 2013 to March 2014, in comparison with 9.6 % in the original data sample we analyzed. However, one needs to keep in mind that this reduction might be caused by varying reasons, such as changes in the labor market environment. Nevertheless, our interview partners confirmed their impression that reducing cycle times significantly contributed to this development.

PIM Card 5 (improving application routing) As proposed, this measure is being implemented in conjunction with PIM Card 1, Application Management Automation , namely in relation to managing job ads master data. Extensive loops and cycle times during application are now controlled by maintaining the responsibility of initial contact partners for timely feedback, even if the application is passed on to colleagues within the business unit. This way, contact partners have an incentive to avoid redundant loops. Outstanding feedback is then escalated to senior management.

4.5 Deployment of tools for empirical process analysis in practice

This section amends the experience report on our sample case with practical challenges observed when using available technology to drive the empirical analysis of process data. We believe that our sample scenario is fairly typical in this regard and that the issues described may be useful for the further development of corresponding tools and systems. In our empirical analysis, we used three types of technology: first, a process-aware ERP system, second, a process mining tool, and third, a tool for statistical analysis.

When using the ERP system of our sample process as a source of information for process improvement, we found it rather challenging to extract and interpret empirical data on process enactment. The major issues in this respect lay in the complexity of the underlying data model, which was partly subject to customization, its available documentation, and the availability of corresponding analysis and extraction reports. From our perspective, the usability of ERP systems in this respect might be improved by providing corresponding standard reports aiding systems administrators. In particular, this issue pertains to combining all relevant tables for particular application scenarios and to the matching of events to underlying process instances. In our case, the latter issue was exacerbated by the use of differing primary keys in related database tables. We are not in a position to judge whether the resulting complexity of the data model is really required. However, we believe that investigating its actual necessity might be worthwhile.

With respect to available process mining tools, we found that there are still certain functions that might be integrated in such systems to improve their effectiveness. This relates not only to process improvement projects, but also to other settings such as compliance management [ 22 ] or benchmarking [ 30 ]. However, we wish to stress that these issues pertain to commercially available tools in general. Disco, the tool used in our project, was chosen as it represents the state of the art of commercially available tools, in particular regarding ease of use and speed of deployment. Beyond the issues discussed here, [ 31 ] comprises a more detailed summary of process mining success factors based on multiple case studies. The following topics should be considered as functions not yet fully implemented:

Pattern analysis allows comparing multiple process enactment variants [ 32 ] including their actual frequency. For example, with regard to repetitive loops (cf. PIM card 5), this functionality would be very useful to identify and prioritize process variants that should be restricted or eliminated.

Compliance rules modeling allows describing relevant regulations for business processes in a way sufficiently formalized to automatically check whether these regulations have been adhered to in a process enactment data sample [ 22 , 33 ]. As an example, consider the requirement of obtaining approval before job offers are issued.

Approximation of manual effort facilitates amending event-based process maps with the underlying manual processing effort. This would tremendously enhance the utility of corresponding analyses and could be achieved by enriching event types with assumptions on the corresponding activities. By matching a material sample log against total capacity used for processing (the so-called baselining in practice), the required degree of validity for the assumptions made could be achieved.

Automated regression analysis allows finding correlations between characteristics of data samples (e.g., between the number of contact partners involved and cycle times). If characteristics are derived from PIOs, respective drivers for process improvement can be identified automatically.

Sample delineation addresses the issue of restricting a data sample to exclude process instances without particular characteristics, such as the presence of start and end events. Since this topic is important to ensure the validity of analyses, tool developers might want to consider guiding users through the sample delineation procedure by way of an appropriate user interface.

Out of the topics listed above, compliance rules modeling and sample delineation can also be addressed through pattern analysis , which constitutes the basic functionality to enable process enactment optimization. Like in our sample case, process improvement projects utilizing empirical process enactment data will employ spreadsheet applications if pattern analysis is not readily available in a process mining tool.

In addition, we used Minitab as an exemplary statistical tool to support process improvement, e.g., with regard to analyzing the correlation between cycle times and candidates’ probability to withdraw their applications. This class of tools can be considered as advanced today and will generally provide accurate implementations of the relevant statistical tests.

5 Related work

Besides approaches directly addressing the topic, the assessment of PIPs relates to a broad array of fields. These range from general process improvement and quality to considerations on empirical research on information systems and are shortly described below.

5.1 Validation of process improvement patterns

Approaches aiming at empirical validation of PIPs can be traced back to quality management and business process reengineering approaches which have evolved since the 1950s and the early 1990s, respectively.

In terms of quality management, Six Sigma [ 27 ] is particularly interesting because it aims at eliminating errors in manufacturing processes through a set of quantitatively oriented tools used to identify and control “sources of poor quality.” While the scope of BPM usually lies in administrative processes instead, there are important analogies since Six Sigma is based on step-by-step optimization of production processes through experimental changes to parameters. This means that measures are subject to a priori assessment before they are implemented.

Business process reengineering (BPR), as exemplified in [ 6 , 34 ], aims at optimizing processes “in the large” instead of implementing incremental PIPs. Transferring process enactment to an external supplier or customer constitutes a good example of this paradigm. While the potentials of this disruptive approach may seem tempting, more recent empirical evaluation has shown that the risk of projects failing is substantial [ 35 ]. Thus, incremental implementation of individual PIPs remains a valid approach.

In contemporary BPM [ 8 ], proposes a framework to select and implement redesign practices. As opposed to our research, this approach does not aim at assessing individual PIPs for a specific applications scenario, but at efficiently appraising a broad framework of practices in order to identify the most relevant propositions. We used earlier results from the same authors as a source of PIPs to be assessed in more detail [ 2 ].

The same authors also developed an approach to appraise BPR practices [ 36 ] based on the analytic hierarchy process (AHP) [ 37 ]. This proposition aims at ranking PIPs (or “best practices”) according to their importance for the organization. This enables limiting further considerations to a prioritized set of PIPs. In contrast, the goal of our approach is to assess individual PIPs for a given application scenario based on a total set of PIPs that should, in the end, be as large as possible. However, we believe that the approach of step-by-step refinement of organizational objectives and PIOs might be used as input to the AHP in terms of scenario-specific impact criteria.

In addition, [ 38 ] proposes PIPs to tackle the findings of an earlier case study on workflow implementation regarding issues with team collaboration. While it is not the objective of this paper to document a general methodology, it can nevertheless be viewed as an approach to prospectively appraise the implementation of PIPs for a particular application scenario.

5.2 Identification of process improvement patterns

In the following, we shortly summarize the relevant state of the art with regard to identifying PIPs that may be subject to assessment.

Besides leveraging PIPs that emerged from the BPR wave of the late 1990s and early 2000s, there also exist more recent attempts to provide a basis for process improvement by appraising perspectives on business process quality based on the software quality [ 3 , 39 ] or managerial analysis and control [ 4 ]. These approaches result in sets of quality attributes or characteristics for business processes. Since measures that aim at fulfilling quality characteristics constitute process improvement measures, quality characteristics can be viewed as PIPs as well. In this context, comprehensively validating the set of quality characteristics provided by an approach through empirical analysis remains challenging, because it will be virtually impossible to find practical cases where the entire set of quality characteristics “adds value.” In this regard, the present approach instead enables organizations to validate quality characteristics to be improved specifically for a particular application scenario.

Benchmarking constitutes an approach widely employed in practice today [ 30 ]. Organizations seek to identify “best practices” to improve their business processes by comparing implementation options and results with “peers.” With regard to specific industries or application fields, the resulting “best practices” have also been compiled into specialized frameworks for process management and improvement such as the IT Infrastructure Library (ITIL) for information management [ 40 ].

In general, contemporary quality management methods used in manufacturing and logistics (e.g., Poka yoke to eliminate potential sources of errors [ 41 ]) can provide interesting pointers for improving business processes. A respective summary of the state of the art in “total quality management” (TQM) can be found in [ 42 ]. As an example for a TQM approach, the European Foundation for Quality Management (EFQM) proposition for achieving “organisational excellence” can be based on a business process perspective [ 43 ]. However, the underlying evaluation dimensions, which can be used to identify process improvement potentials, are rather abstract and require general and scenario-specific interpretation.

Note that research on PIPs addresses the quality of process models and process implementations in the sense of business content . In contrast [ 44 – 49 ], exemplify propositions addressing process model quality in terms of structure, comprehensibility etc., i.e., the proper representation of actual business content by model elements.

5.3 Additional aspects

In IS research, there have been diverse propositions to ensure common standards of scientific rigor in empirical research such as field experiments or case studies [ 50 , 51 ]. Hevner et al. [ 52 ] summarized empirical “design evaluation methods” for information systems research. As a basis of this paper, we chose the requirements summary by Wieringa et al. [ 24 ] due to its concise, checklist-based character, which makes it readily applicable to research as well as to discussion with practitioners.

Gregor [ 53 ] provided a taxonomy on various types of theory in information systems research. In this context, PIPs would fall in the category of “design and action” theories since they give prescriptions on how to construct artifacts (in this case, business processes). This perspective is interesting for the purposes of this paper, since it highlights the limitations of treating a PIP as an object of validation, and hence as a theory, on its own. Rather, whether a PIP is valid as a prescription to implement or change a business process clearly depends on the relevant application scenario and organizational context. In line with our propositions, a corresponding scenario-aware assessment method is required. This method then constitutes a “design and action” theory.

The top-down approach of deriving PIOs and PIMs from organizational objectives is similar to techniques for eliciting requirements in goal-oriented requirements engineering such as KAOS [ 54 ] or i* [ 55 ]. Propositions in this respect are based on working with stakeholders to identify goals to be met by a system [ 56 ]. Goals are refined step by step until a level is reached that is suitable for technical implementation. We stipulated that a structure of PIOs based on organizational objectives is useful to avoid redundant discussions of the business value of lower-level PIOs. Similarly, the state of the art in requirements engineering recognizes the practical benefits of a “goal refinement tree” linking strategic objectives to detailed technical requirements [ 57 ]. In this respect, the concept of organizational objectives compares well to the notion of “soft goals” [ 58 ]. The step-by-step refinement of PIOs corresponds to the basic AND/OR decomposition of goals which has been extended to common notations for goal documentation such as KAOS [ 54 ]. These parallels are based on the shared underlying notion of breaking down a larger problem, such as overall cost improvement, into more manageable chunks. This principle can also be found in contemporary approaches toward project management, e.g., in software implementation [ 59 ].

6 Discussion

When motivating this paper, we identified three challenges to be addressed in order to enable a priori PIP assessment. This section discusses how we addressed these challenges. Further, it discusses relevant limitations of our approach and presents recommendations that may be useful for similar projects. For quick reference, Fig.  12 recapitulates our proposition as a simplified approach overview: We first seek to gain a profound understanding of the considered application scenario including the corresponding organizational objectives. These are then refined into PIOs that are sufficiently granular to allow identifying relevant PIPs in the next step. Finally, relevant PIPs are bundled into PIMs and are appraised to enable implementation decisions.

Approach overview

6.1 Revisiting research challenges

In the previous sections, we described an approach toward a priori PIP assessment. With respect to Challenge 1 (cf. Sect.  1.1 ), we believe that this approach is better suited to reflect common practice in the field than the available state of the art in IS research (cf. Sect.  5.1 ). While there have been propositions toward ex-post empirical validation of PIPs in the past, to our knowledge, the approach presented in this paper constitutes the first proposition toward a priori assessment of PIPs in the area of BPM. In particular, the two perspectives on PIP appraisal differ in their treatment of the available set of PIPs:

The ex-post perspective seeks to narrow down the set of PIPs to a selection of aspects with demonstrable empirical relevance in a wide variety of application scenarios, thus following common scientific practice.

The a priori perspective seeks to accommodate a comprehensive set of PIPs, but limits assessment to one particular application scenario. It thus “constructively validates” only a limited set of PIPs at a time.

Without doubt, the first perspective reflects common scientific practice, since it enables generic statements on PIPs that are independent of a particular context. Nevertheless, we found that the second perspective tends to be more in line with the expectations of practitioners. In our opinion, this reflects a central characteristic of PIPs and the corresponding implications for their practical adoption: As becomes clear when considering PIOs for various application scenarios, characteristics that drive organizational objectives may differ substantially for varying sample processes. Hence, a validation of PIPs based on other application scenarios is of limited value for implementation decisions. In this context, the practitioners we interviewed observed that a preselection of PIPs will be effective only in the case of frameworks addressing a particular field of application. As examples, consider industry-specific “best practices” such as ITIL for the field of information management [ 40 ], or guidelines for dermato-oncology in medicine [ 60 ].

Assessing PIPs for each individual project requires substantial effort by qualified personnel to understand the application scenario, identify and refine organizational objectives and PIOs, select appropriate PIPs, and finally bundle them into implementable PIMs. Whether this effort can be justified before initiating the assessment depends on the creation of business value that may be reasonably expected. Organizations should consider three topics before starting a PIPs assessment project:

Is the business process substantially relevant to the organization, e.g., with regard to the output produced or the cost volume incurred?

May the organization assume that there are improvement potentials in the process, for example, when considering existing problem reports or benchmarks [ 30 ]?

Are there particular circumstances that require analyzing the process anyway such as, in our case, intentions to replace the underlying PAIS?

In our sample case, we could assume an overall annual process cost of about 11.6m Euros (cf. Sect.  4.1 ). Thus, it became clear that even minor cost potentials identified would suffice to cover assessment effort.

Based on these observations, we believe that our approach toward PIP assessment is better aligned with common practice in the field and thus better suited to address Challenge 1 (cf. Sect.  1.1 ) than the previous state of the art.

Regarding Challenge 2, evaluating our approach through a substantial experience report, Sects.  2 and 4 presented the real-world case we used to this end and the results of applying our propositions. The sample case dealt with a business process found in most organizations and comprised 27,205 cases managed through a standard ERP system. It exposed typical issues when dealing with empirical analysis of real-world process enactment data, such as the complexity of extracting and interpreting data, as well as relevant process characteristics that do not become apparent by analyzing transactional data. We thus stipulate that our experience report represents common practical problems fairly well and has been suitable to evaluate our proposed approach.

To address Challenge 3, the reconciliation of our propositions to applicable scientific standards, we used a framework by Wieringa et al. [ 24 ] to guide the description of our approach. This enables us to trace all relevant components of an approach that fulfills scientific criteria, and simplifies the appraisal whether the corresponding requirements can be considered as fulfilled. We found that the more rigorous documentation of problem statement and research design demanded by scientific rigor required some additional effort in comparison with what is usually found in practice. However, this task proved still worthwhile, since it simplified final discussion of proposed PIMs with stakeholders. As an example, consider the impact of cycle times on the probability of candidates to decline job offers, which could only be demonstrated through rigorous statistical testing.

Overall, we consider our research challenges as met based on the considerations made above. However, there are still some relevant limitations we discuss in the following.

6.2 Relevant limitations

We demonstrated the application of our approach on the basis of a substantial real-world business process with 27,205 process instances. Nevertheless, the first issue that needs to be discussed with respect to limitations of our approach pertains to its evaluation on the basis of only one application scenario and thus a limited set of relevant PIPs. As a limitation to the environment of data collection (cf. Sect.  3.2 ), applicants could not be interviewed because of privacy regulations. It will thus be useful to apply the approach to additional scenarios to extend the set of PIPs actually applied. Of course, this will require access to additional real-world process improvement projects with substantial sets of empirical data on business processes. To draw meaningful conclusions, these processes should be comparable to what is commonly found in other organizations. Accordingly, additional experience reports (with the potential to extend the underlying approach) shall be an issue for future work taking up such opportunities.

A second topic relates to the availability of a comprehensive set of PIPs to be applied to PIOs. In principle, this does not affect the validity of our approach. However, it impacts its practical effectiveness, since it will determine the actual business value of PIMs identified. In this respect, much work has been undertaken by Reijers and Limam Mansar [ 2 , 8 ], but the matter of conceptually deriving a comprehensive set of PIPs, which might be amended with more concise additions for specific application fields, still remains an open issue. We intend to contribute to this topic in the course of our ongoing work on business process quality [ 4 ] by identifying applicable quality attributes for business processes.

On a more abstract level, the third issue pertains to methodological limitations with respect to empirically validating PIPs. In this context, PIPs can be viewed as a prediction or theory dealing with the impact of certain process characteristics on process performance. However, comparable to design patterns in software engineering [ 9 ], PIPs do not constitute a self-contained concept for the following two reasons. As discussed above, currently no approach is available to demonstrate that a set of PIPs is comprehensive. In addition, the degree of utility of any given PIP is highly specific to the application scenario considered. Thus, it is virtually impossible to validate an entire set of PIPs by means of empirical information systems research such as field experiments, participative research, or case studies [ 61 ]. As discussed in Sect.  5 , researchers have addressed this issue by conducting meta-studies on a broad range of PIPs [ 8 ]. This, however, means that individual PIPs are validated based on widely varying research designs. The approach presented in this paper also cannot resolve this issue. Still, it constitutes a generally applicable and reusable approach to assess PIPs for given application scenarios, which can contribute to harmonize PIP appraisal designs.

A fourth issue that needs to be discussed concerns inherent limitations with regard to demonstrating the general validity of the assessment approach proposed. The approach results in recommendations on which PIPs to implement. However, the question is how we can ensure that these recommendations are well founded. This challenge is exacerbated by two topics:

On a more detailed level, the business value of PIPs is appraised considering the business process and the scenario addressed. That is, the general assessment approach is refined specifically for each application scenario. Thus, it is not possible to fully replicate the same assessment approach in other settings, which limits the possibility of empirical validation. In other words, the validity of predictions on the business value of a particular PIP in a particular setting cannot provide assurance on the validity of predictions on other PIPs in other settings.

Revisiting PIMs after implementation will only allow identifying “false positives,” i.e., PIMs that did not deliver the business value expected. “False negatives,” i.e., PIPs not chosen for implementation which would have delivered a positive business value, will always remain undetected.

Nevertheless, it is still good organizational practice to track the results of PIM implementation. This provides an incentive to involved stakeholders to apply due diligence during PIPs assessment. However, since only “false positives” can be tracked, one should be aware that this might lead to overly risk-averse assessment practices. Setting top-down process improvement targets (e.g., via quantitative benchmarking) can be a way to respond to this challenge.

A final limitation takes up the issue of “false negatives” described above. It pertains to the degree of control we have with regard to the procedure of selecting PIPs and proposing PIMs for an application scenario. We have to be aware that this procedure depends on the knowledge, experience, and creativity of project participants. In other words, if no project participant can think of a way in which a PIP could be used to address a PIO, the PIP will not be considered in PIM propositions. However, this does not mean that the PIP cannot provide value in the application scenario. We stipulate that the step-by-step refinement of PIOs is a useful technique to address this issue since it helps to focus efforts on relevant aspects. However, it cannot provide formal assurance on this issue.

6.3 Recommendations for implementing our method

When working with practitioners to identify and assess PIPs applicable to our sample scenario, we encountered several general issues and recommendations that should be considered when applying PIPs in process improvement projects. We discussed these observations with our interview partners in the course of the respective steps in our approach. On that basis, we phrased a number of project recommendations that we present in the following. These recommendations were reconciled with management level interview partners and may be viewed as guidance for researchers and practitioners when setting up and executing comparable projects. Readers familiar with these topics may wish to skip this section.

Our first recommendation pertains to the overall structure of the proposed approach and to the “research design” component as required in [ 24 ].

Project Recommendation 1 (top-down process improvement methodology) Top-down process improvement refers to methods based on an initial definition of and agreement on the goals to be pursued, which are then further elaborated and amended with corresponding measures in a step-by-step approach. As a general principle, earlier decisions are refined to a more detailed level in later project phases. Top-down approaches address challenges resulting from two topics: First, process improvement projects typically require effective collaboration between multiple parties in an organization. However, these may tend to “sub-optimize” by focusing on individual interests instead of overall organizational objectives. As an example, consider the recruiting department and the various business units in our sample scenario. To “sub-optimize” its own effort in application handling, the recruiting department might pass applications not to the best, but to the most accessible contact partner in a business unit, thus impeding the goals of the organization as a whole. To realize the full potential of process improvement, parties need to be aligned toward clearly defined common goals and decisions as early as possible. Second, projects without a top-down decision structure may be obstructed by re-discussing goals and decisions multiple times. Besides the additional effort caused, this may lead to inconsistencies in the project. As an example, consider multiple measures addressing cycle times. Without a general understanding that cycle times are an objective of process improvement, this discussion will be led for each corresponding measure individually. With a top-down approach, earlier goals and decisions can serve as a gauge to appraise later decisions and measures.

The top-down principle is reflected in our approach. First, we require an early senior management agreement on the general “call for action” (see the concept of organizational objectives), which is then refined into process-related objectives (see the PIOs concept), and finally into individual improvement measures. This way, the discussion of individual measures focuses on how things are to be achieved instead of what to achieve in general. To implement this recommendation, agreed project results should be strictly documented, e.g., in a decision log.

The second recommendation is applicable to the “unit” and “environment of data collection” components as described in [ 24 ].

Project Recommendation 2 (identification of potential PIOs, PIMs, and PIPs based on process design and enactment) Potentially applicable PIOs, PIMs, and PIPs should be identified not only by considering the process model, but also by analyzing empirical data on actual process enactment. This is crucial to focus on topics of actual value potential. For example, consider the selection of critical cases in Fig.  3 , which is reflected in the PIOs for our sample case.

The third and fourth recommendations address data gathering and analysis procedures required to appraise PIOs and PIMs for a particular application scenario. In terms of [ 24 ], they qualify “measurement” and “data analysis procedures”.

Project Recommendation 3 (appropriate qualitative or quantitative demonstration of business value) For each PIO to be addressed by PIMs, the underlying business value must be empirically demonstrated based on proper qualitative or quantitative analyses with respect to organizational objectives. Likewise, the business value of PIMs must be made transparent through appropriate analyses.

The specific analytic approach for individual PIOs and PIMs must consider the actual application scenario, balancing expected insights against analysis efforts. For example, the omission of tasks that obviously do not contribute to the business objective of the process can be justified by a short qualitative description. In contrast, the introduction of additional control tasks to diminish defects later on in the process will require careful quantitative weighing of pros and cons.

Project Recommendation 4 (identifying relevant stakeholders as interview partners) To ensure the validity of measurement procedures, proper selection of interview partners is particularly relevant for PIOs and PIMs that should be validated qualitatively. For BPM scenarios, it is important to interview experienced senior personnel overlooking the end-to-end business process and to represent both the “supplier” and the “customer” perspective to avoid lopsided optimization. For our sample process, we interviewed the head of recruiting operations and the administrator of the application management process from the “supplier” side, and the HR partner of a business unit as well as team managers from the business unit from the “customer” side.

The fifth and sixth recommendations concern the final assessment of PIMs. Hence, they refer to “data analysis procedures” as well [ 24 ].

Project Recommendation 5 (considering implementation effort in business value appraisal) When discussing the business value of particular PIMs for a business process, the respective implementation effort must be taken into account. This includes measures required, cost, time, and change management issues (e.g., training personnel to enact new activities). A PIM will only provide business value if implementation effort is justified by realized process improvement potentials. For example, an organization may demand that the required investment must not exceed three times the projected annual cost savings when appraising operational cost optimization measures.

Project recommendation 6 (leveraging “quick win” potentials) In many practical scenarios, it is possible to identify “quick win” PIMs that can be implemented with limited effort and should thus be given higher priority than others, in particular in comparison with full-scale PAIS implementation measures which are usually very costly. Examples include the elimination of process defects caused by process participants’ behavior, interface issues between departments, or issues of data quality. Note that these topics are often identified through empirical analyses (e.g., using process mining).

7 Summary and outlook

This paper described an approach for a priori, scenario-specific assessment of process improvement patterns based on organizational objectives, process improvement objectives, and process improvement measures. In our approach, we leveraged available work on generic requirements toward empirical research in IS engineering [ 24 ]. We thus demonstrated how these principles can be applied to practical cases while ensuring the general appeal of our approach.

We reported on the application of the approach to a real-world business process, including validation of the respective results with practitioners. The approach led to the identification of five potential process improvement measures that bundle and refine individual process improvement patterns for the given application scenario. Matching the expected gains against implementation and operating efforts, the organization was enabled to take well-informed implementation decisions. Revisiting the proposed process improvement measures more than 1 year after initial data collection confirmed that these decisions could be used to guide further development of the business process in practice.

In future research, we will integrate our PIP assessment approach into a broader proposal to manage the quality of business processes [ 4 ]. Moreover, we will apply it to further real-world application scenarios to gain additional experience. This will enable us to further validate and elaborate the approach. The assessment of PIPs as well as the validation of PIOs and PIMs will always require to refine our general approach according to the application scenario. However, considering additional sample scenarios might enable us to define standard types of assessment and validation procedures, for example, based on corresponding types of PIOs that occur in multiple application scenarios. In turn, this may further facilitate the practical adoption of our approach.

The field of process intelligence deals with analyzing the actual enactment of business processes [ 17 ]. In this context, process mining refers to using processing events logged with a timestamp to generate process maps , i.e., graphic representations of actual process enactment traces, and additional process information [ 11 ]. Note that Disco was selected as a representative of a number of tools available to practitioners in commercial settings today. Alternatives like ProM [ 18 ] or Celonis Discovery [ 19 ] might be used as well.

Note that in the given context, the term “measure” is not to be understood as a means of measuring something (e.g., a performance indicator) or as a unit of quantity, but as a coordinated set of activities.

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Lohrmann, M., Reichert, M. Effective application of process improvement patterns to business processes. Softw Syst Model 15 , 353–375 (2016). https://doi.org/10.1007/s10270-014-0443-z

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This paper aims to examine the relationship between business process management (BPM) and company performance. The research focuses on the instrumental aspect of core business processes and its controlling activities in small and medium-sized companies (SMEs) to identify the relationship to company performance.

Design/methodology/approach

The results presented in this paper are based on a survey of Slovene SMEs. A questionnaire was distributed to 3007 SMEs via e-mail and a response rate of 5.42% was achieved. The financial data of companies over a six year period as derived from the publicly available financial reports of SMEs along with an industry-specific financial risk measure and other financial data were used for the company risk-adjusted performance measures of relative residual income (ROE- r ) and risk-adjusted ROE (ROE-a) calculation.

The results show that instrumental aspects of core business process controlling activities are related to risk-adjusted company performance measures ROE- r and ROE-a. Companies with lower ROE- r and ROE-a have been perceived to be more focused on the instrumental aspect of BPM. Presumably due to the small sample, the results of a non-parametric Mann–Whitney U test did not statistically confirm the developed hypothesis: “the instrumental aspect of controlling as a core process management activity has a statistically significant impact on company risk-adjusted performance measures such as ROE- r and ROE-a.” Despite this, the results show a possible negative correlation between risk-adjusted performance measures and BPM, which opens possibilities for further research.

Research limitations/implications

The main limitation of the purposed study model is that the paper have studied only control activities of core business processes and relate it to company risk-adjusted performance measures. The study has been limited by the SME sample and the use of a survey as a research instrument. An additional limitation of the research is the degree of reliability implied by the assumptions of the models used to estimate the required return on equity and risk. Results concern investors, managers and practitioners to start BPM improvement initiatives, to set BPM priority measures and to set priority management decisions and further actions.

Originality/value

This paper presents the unique findings from an investigation of the instrumental aspects of BPM practices and their relationship to company risk-adjusted performance measures in SMEs. This paper developed a measurement instrument for measuring the instrumental aspects of BPM use. An additional original contribution is the use of company risk-adjusted performance measures such as ROE- r and ROE-a, which take into account the required profitability of companies in different industries according to the risk and allows comparable results of companies from different industries. The approach is innovative and interesting as regards researching the factors that affect the profitability of companies that operate in different industries.

• Risk management
• Measurement
• Business systems
• Core processes
• Performance
• Risk-adjusted performance measures

Gošnik, D. and Stubelj, I. (2022), "Business process management and risk-adjusted performance in SMEs", Kybernetes , Vol. 51 No. 2, pp. 659-675. https://doi.org/10.1108/K-11-2020-0794

Emerald Publishing Limited

Copyright © 2021, Dušan Gošnik and Igor Stubelj

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The competitiveness of every company arises from the competitiveness of its business processes. Business processes determine the quality, innovation and productivity (efficiency) of companies ( Potočan and Mulej, 2009 ; Minonne and Turner, 2012 ). Business processes in any organization determine operating costs and affect business performance ( Seethamraju, 2012 ). We can define the business process as a comprehensive and dynamic coordinated set of connected activities, from purchasing to sales, which are intended for the appropriate supply of customers and enable a successful business performance of a company in a particular economic environment ( Janeš et al. , 2017 ; Janeš et al. , 2018 ; Novak and Janeš, 2019 ). It follows that, in accordance with the need for process orientation, each company should plan, organize, lead and control its business processes. It is described as business process management (BPM). Among all the business processes of a company, the core business processes contribute to company performance the most ( Thennakoon et al. , 2018 ; Zelt et al. , 2018 ). If the core processes are innovative, this will be reflected in the performance of the company ( Thennakoon et al. , 2018 ; Gošnik , 2019a, 2019b ). From this perspective, the management of core processes in companies should be the priority focus of managers and company owners. Previous research studies on this field show us some relations between core business processes and company performance and that a lack of measurement at BPM implementation results in less successful implementation of BPM ( Gošnik et al. , 2016 ; Stojanović et al. , 2017 ; Gošnik , 2019a, 2019b ). Company performance in these researches was not related specifically to risk-adjusted company performance measures ROE- r and ROE-a, which has been detected as a research gap and is attracted our focus in this research.

BPM is a complex area which includes the management activities of planning, organizing, leading and controlling of business processes and is influenced by a number of factors, which cannot all be included in this research. We will take into consideration control activities of core business processes and relate it to company risk-adjusted performance measures. The goal of this research is to examine how controlling activities of core business process are related to risk-adjusted company performance measures ROE- r and ROE-a We have tested hypothesis: “the instrumental aspect of control as a core process management activity has a statistically significant impact on the company risk-adjusted performance measures ROE- r and ROE-a.” Methodology used for data analysis was Bartlett's test, Kaiser–Maier–Olkin test (KMO) and Mann–Whitney U test.

This research presents the unique findings from an investigation of the instrumental aspects of BPM practices and their relationship to company risk-adjusted performance measures in SMEs. We developed a measurement instrument for measuring the instrumental aspects of BPM use.

Paper is organized as follows: literature overview, BPM and instrumental aspect of organization, the overview of company performance and company risk-adjusted company performance measures and hypothesis development. An empirical study with results and discussion is presented with the limitations and further research possibilities described.

2. Literature review

2.1 business process management and core business processes.

interests of customers, suppliers, employees; and

instrumental aspects (interest of company owners and managers) ( Tavčar, 2009 ; Burlton, 2010 ; Vom Brocke et al. , 2014 ; Trkman et al. , 2015 ).

2.2 Instrumental aspect of an organization

Every organization is an instrument (a machine, device) for achieving objectives and is subordinate to the interests of the owners and founders ( Stalk et al. , 1992 ; Inkpen and Choudhury, 1995 ; De Wit and Meyers, 2005 ; Tavčar, 2009 ). This is also reflected in the management of a company and the core activities of management (planning, organizing, leading and controlling). Employees and processes are monitored continuously, with an emphasis on costs, productivity and maximizing short-term profit rather than the long-term growth and development of the organization ( Tavčar, 2009 ). It is reflected in the quantitative measurement of processes and company performance. According to Trkman (2010) , unsuccessful implementation of changes in the business processes are connected to the fact that management does not take into account instrumental aspects. Therefore, our research in the empirical part focuses especially on the instrumental aspects of controlling core business processes and the effect of this on company performance.

2.3 Business processes and company performance

Nandakumar et al. (2009) define a company’s performance as the degree to which objectives are achieved (profit) and success in relation to the competition. Berends et al. (2016) interpret a company’s performance in terms of reputation and capacity to adjust to the changes of the environment and suggest that performance measuring would include several measures: financial, operational and comprehensive. Various authors ( De Wall, 2008 ; Strecker, 2009 ) suggest the inclusion of periods from one to three years and consideration of the average values of individual measures to achieve a more objective assessment.

When we compare the performance of companies between different industries the use of profitability ratios across industries without risk adjustment is not appropriate. Investing in companies is risky. Investors can avoid part of the risk by diversifying their investments, but they cannot completely avoid risk. According to the well-known and widely used risk and return model in practice, which is the capital asset pricing model or CAPM (which is used and presented later in the paper) the average investor is risk-averse which means that he must be compensated for bearing risk. He seeks investments that have the best-expected risk/return ratio. This creates a link between risk and return. To obtain the required capital, companies must prove that they can achieve the appropriate profitability for the level of risk. Only if a company’s return on capital is higher than the required return that accounts for risk, will the investment add value for owners. In view of the above, the only way to correctly examine the relationship between BPM and company performance across different industries is to use risk-adjusted performance measures.

2.4 Company risk-adjusted performance measures

To measure the efficiency of invested capital, a company’s profitability ratios such as profit margin, basic earning power, return on assets and return of equity are used in practice. In addition, to evaluate the management performance of non-listed companies (for which the equity market value is unknown) in achieving the purpose of capital companies, which is to increase the value for owners, the criterion usually used is profitability ratios. This is suitable if we compare companies from the same or similar industries that have a similar level of risk.

Based on the irrefutable fact that profitability and risk are correlated, the required return of a business depends on the level of risk involved. In accordance with this fact, we will test management approaches with two risk-adjusted profitability measures based on return on equity (ROE).

2.4.1 Residual income approach as a risk-adjusted equity capital performance measure.

Finance theory says that the primary goal of managers is to increase value for the equity owners of companies [ 1 ]. In practice, this means increasing the value of assets, which leads to growth in the value of equity. However, to measure management performance, we need criteria that tell us whether management increases the value of equity. Managers also need such measures to make decisions. These criteria should take into account the fact that value-added for owners is created only when the expected return is higher than the required one, which takes into account the risk. Glen (2005) argued that managers will not be able to define the consequences of their decisions without being aware of this. The concept of residual income as a performance measure and valuation tool could be used. This was introduced in the early 1920s and infrequently used since, despite its interesting underpinning. Stewart’s publication in 1991, in which the authors presented their “modernized” version of residual income referred to as economic value added or EVA®, has renewed interest in the concept ( Christensen et al. , 2002 ). An interesting contribution of this model is the aspect that a company’s positive net income does not necessarily imply that a company is creating value for its owners.

We start from the residual income valuation model (RIV) that is an appealing approach and which received attention in the accounting literature for its apparent ability to give a constructive role to accounting data in equity valuation. Many researchers have explored the pros and cons of the RIV model as a useful valuation tool [ 2 ] as proved by many publications. We will use the concept of residual income as a relative measure of risk-adjusted return.

We can currently estimate residual income with the following equation ( Halsey, 2001 ): (1) R I 0 = E 0 - r · B V - 1

where RI 0 is the present value of residual income, E 0 is the present value of net income, r is the required return of equity capital, BV −1 is the book value of equity capital in the previous period. The value of expected residual incomes can be expressed as: (2) R I 1 r = E 1 - r · B V 0 r

The value of equity capital with constant growing expected residual income can be calculated as: (3) V 0 = B V 0 + R I 1 r - g R I = B V 0 + E 1 - r · B V 0 r - g R I

where RI 1 is the expected residual income, E 1 is the expected net income, BV 0 is the book value of equity capital and g RI is the expected growth rate of residual income. A company adds value for its owners if residual income is positive. For the purposes of our analysis, we have expressed the relative residual income with the following equation: (4) R I t % = R I t B V t - 1 + B V t 2 = E t - r t · B V t - 1 + B V t 2 B V t - 1 + B V t 2 = E t B V t - 1 + B V t 2 - r t by a simplification based on the ROE calculation equation we have: (5) R I t % = R O E t - r t

where ROE t is return on equity capital for year t , RI t is the residual income for the year t , BV t−1 is the book value of equity capital at the end of the year t −1, E t is the net income for year t , BV t is the book value of equity capital at the end of the year t and r t is the required return of equity capital estimated in the year t. RI t ( % ) is the residual income in per cent or relative residual income which we will denote as residual ROE or ROE- r .

To calculate the relative residual income we need to estimate the required return on equity capital which is the essential parameter for the residual income calculation. Required return on equity was estimated using the CAPM developed independently by Treynor (1961 , 1962 ), Sharpe (1964) , Lintner (1965a , 1965b ), Mossin (1966) . The CAPM equation is: (6) r i = r f + β i · r m - r f

Where r i is a required return of equity i, r f is the risk free rate, β i is the beta coefficient (measure of market risk) of equity i , r m is the market return on equity, and the (r m – r f ) is the market risk premium.

Despite some very strong and unrealistic assumptions [ 3 ], no doubt due to its “simplicity,” CAPM is in practice the most widely used model for determining the required return on equity [ 4 ]. However, the discussion on the validity of the CAPM is still ongoing. Severe criticism and scepticism in relation to the validity of CAPM have been expressed by McGoun (1993) , Fernandez (2015) . In addition, Fama and French (1992) demonstrated that the CAPM does not explain a substantial fraction of market returns.

2.4.2 Systematic risk-adjusted return on equity approach as a risk-adjusted equity capital performance measure.

ROE and market returns are equal in the long-term; and

investors can avoid the specific risk of ROE with diversification and only the systematic risk matters.

We search for the return which can be compared to the market return at a given level of risk. We substitute the required return with ROE and the market return with the adjusted ROE (in the CAPM equation). We rearrange the equation to calculate the company’s risk-adjusted ROE: (7) R O E i ,   A d j u s t e d = R O E i β i + r f · ( 1 - 1 β i )

where ROE i, Adjusted is a ROE of company i, adjusted for the market risk which we will denote as ROE-a, ROE i is a ROE of company i, β i is the measure of the market (systematic) risk for a company and r f is the risk free rate of return.

Performance measures ROE- r and ROE-a derived as explained above take into account differences in systematic risk between industries. Such measures are more appropriate than ROE to examine the relationship between BPM and company performance across different industries.

3. Research methodology

Based on the theoretical background, we relate core business processes to company performance. We focus on the instrumental aspect of controlling the activities of business processes and relate them to company performance, measured with the risk-adjusted performance measures ROE- r and ROE-a.

Based on theoretical starting points in the theoretical part and detected research gap we have developed hypothesis H: ''the instrumental aspect of control as a core process management activity has a statistically significant impact on the company risk-adjusted performance measures ROE- r and ROE-a.’’

whether there are statistically significant connections between core process management activities and the performance of a company;

whether the hypotheses is valid; and

how is the instrumental aspect of controlling core processes related to the company risk-adjusted performance measures ROE- r and ROE-a.

3.1 Data collecting methodology

We used an online questionnaire to collect the data. We developed it through a review of the literature in the theoretical part of the research.

The questionnaire was comprised of several thematic sections. The questionnaire was comprised of closed-ended questions. In terms of the nature of the questions, we included questions of fact. Respondents provided their degrees of agreement with the statements made. We used a six-point Likert scale to avoid responses falling into the middle of the scale ( Easterby-Smith et al. , 2007 ). Within the context of hypothesis, we developed a set of statements that measure the instrumental aspects of controlling core processes. The indicator of influence was the estimated degree of agreement by the respondents to each claim.

The performance of SMEs risk-adjusted measures ROE- r and ROE-a was calculated comprehensively using secondary data from different sources. We calculated the risk-adjusted performance for five years.

3.2 Validation of the questionnaire

In terms of content, the measuring instrument was developed through a review of the literature in the theoretical part of the research. The reliability of the questionnaire was verified using the Cronbach alpha ( α ) coefficient, which is intended to measure the internal consistency of the measuring instrument. The questionnaire was further pre-tested in an academic setting before being sent to the companies.

3.3 Financial data of companies used for company risk-adjusted performance measures

In total, 163 companies responded to our questionnaire. In regard to all companies, the financial data was collected from the balance sheet and income statement of Slovenian companies for the period 2011–2016. For each company, we obtained data regarding net income, financial debt and equity from the Gvin available financial database. For some companies, data was not available for all the years in question (some companies did not operate throughout the entire period analyzed) and we excluded those companies. We also excluded from our further analysis all the companies with negative equity capital. Our final data set with financial data available included 149 companies.

3.4 Data analysis

The relationship (correlation) between variables or assertions within a factor, which we call a factor in the statistical analysis phase, is analyzed and presented with the assistance of Bartlett's test and KMO test.

Mann–Whitney U test.

4. Findings

4.1 research population and sample.

The target population in our survey were SMEs, which at the time of our survey on 11th of January 2017 were in the public database of business entities with headquarters in the Republic of Slovenia (Ajpes). Questionnaires were addressed to all 3,007 SMESs in Slovenia and their general managers. We have asked them to participate in the research or to include their co-workers who have the best insight into BPM practice in the company. That possibility was considered before research start and included in the questionnaire. Respondents in this research were business function managers (35%), general managers (27%), followed by business process owners (7.4%), project managers (4.3%), technical managers (2.5%) and others (23.9%). We assume that questionnaires were fulfilled by the most qualified employees in the SME’s and that results reflect real status of BPM in the SMEs.

We received 163 company questionnaires. Of those, 44.8% were small companies and 55.2% were medium-sized companies. Given the initial sample framework of 3,007 companies, this represents a 5.42% response rate. The majority of the companies included in this research operate in the manufacturing and processing (37.4%), wholesale and retail trade activities (10.4%) and construction (6.7%) sectors. The remaining 45.5% of companies are distributed between other industries.

When we matched the questionnaires with financial data, we obtained a data set of 149 companies. We eliminated 14 companies that had incomplete financial data or negative equity capital during the period in question.

4.2 Instrumental aspects of core business processes – control

In Table 1 , we present the results of the measurement system for measuring instrumental aspects of core business processes control. The main purpose is to ensure that our measurement system is appropriate, which is one of the original contributions of this research.

As we see in Table 1 , companies are using the most common practice; that changes to the core processes are measured by financial effects (4.48 out of 6). In addition, the statement about clearly defined indicators to measure core process changes was rated quite highly (4.02 out of 6). Based on the statements from Table 1 , we have analyzed the relationship between statements ( Table 2 ). An analysis of the relationships between statements ( Tables 2 and 3 ) is important to ensure that our statements for measuring the instrumental aspects of business process control activities are consistent and appropriate for further analysis. An analysis of

Tables 2 and 3 shows the results of the Bartlett test and KMO value.

The results show that the KMO value is 0.614, Hi square = 118,766. The Bartlett test is statistically significant in that it shows us a p -value which is under 0.05. The results show that statements for measuring instrumental aspects of business process control activities are appropriate for further analysis.

Statements for measuring instrumental aspects of core business processes are well-correlated, demonstrating a correlation analysis with values higher than 0.3 ( Phanny, 2009 ) ( Table 3 ).

The determinant is 0.395, which is more than 0.00001. According to Yong and Pearce (2013) , our statements for measuring the instrumental aspects of core business processes – control are consistent and appropriate for use in further research.

4.3 Estimation of input variables and company risk-adjusted performance measures

4.3 1 input variables estimation..

For the calculation of company risk-adjusted performance measures, we estimated the risk-free rate of return and the market risk premium for the Slovenian financial market and the systematic risk measure for each company. Estimating this variables practice is not straightforward due to the lack of an ideal method, and is an especially challenging task on a capital market like that of Slovenia [ 5 ]. A practical solution is the use of data from a developed capital market with adjustment. In our empirical analysis, we used all data from the US market as we found coverage for all the analyzed years and then we adjusted this data for the Slovenian capital market. We did not mix data from different markets to prevent additional bias.

We estimated a long-term equilibrium risk-free rate of return for every observed year as the average yield to maturity of U.S. indexed bonds (30-Year 3%–7/8% Treasury Inflation-Indexed Bond, Due 4/15/2029) [ 6 ] of the last ten years (monthly data). According to the European Central Bank (2020) target inflation rate [ 7 ], we added the expected inflation of 2% to obtain the nominal risk-free rate of return.

We used the average of the implied market risk premium estimations from Damodaran (2020) in the past 10 years before each of our observed years. The fluctuations of the risk aversion between the observed years are incorporated in the implied market risk premium. We also assumed that investors have the option to sweep away country-specific risk with global diversification, and consequently the capital market does not reward investors with additional country risk premium:

We assumed a long-term sustainable market risk premium of 4% [ 9 ] to which we added a country risk premium for each year. To obtain the country risk premium we used the relative volatility of stocks versus bonds market which we multiplied with the average of credit-rating estimated default swap, and a credit default swap for Slovenia net of United States credit default swap [ 10 ]. A country risk premium is not theoretically supported in the CAPM [ 11 ], however, it is widely used in practice. The rationale for our second approach is that a long-term market risk premium is stable over time, with short-term fluctuations in the investor’s risk aversion. We account for these fluctuations through a country risk premium with the logic that the greater the aversion to risk on the capital market, the greater the country risk premium and consequently the market risk premium.

For the measure of risk, we used betas (i.e. market risk measures) of the US companies, which can be accessed at Damodaran (2020) [ 12 ]. In this analysis, we apply sector-level data for unlevered betas, calculated for all the observed years (2012 to 2016), by which we translated the industry sectors used by Damodaran (2020) to the Slovenian NACE Rev. 2 industry classification. For each industry in this classification, we calculated the average unlevered betas. In the next step, we used average unlevered industry betas to calculate the firm-level leveraged beta by applying the Hamada equation and adapting beta for relevant tax rates on profit and the company’s debt-to-equity ratio. The Hamada equation is: (7) β l = β u · 1 + 1 - T · w d w s

where β l is the leveraged beta for the company, β u is the unleveraged beta for the industry, w d and w s weights of equity capital and debt, where w d + w s = 1, T is the corporate tax rate. The end estimated parameters used for the CAPM are presented in Table 4 . Calculations were made based on data from Damodaran (2020) , Fred (2020) , European Central Bank (2020), Bloomberg (2020) and Gvin (2020) .

Table 4 shows the estimated parameters for the Slovenian market (in accordance with the explanation above) in the analyzed years that were entered in the CAPM model.

4.3.2 Adjusted performance measures estimation.

We estimated all the parameters in accordance with the theoretical basis and methodology described in the previous chapters. In Tables 5 and 6 , we present the average values in comparison with the aggregated Slovenian company data. In further analysis, individual companies are used.

As we see from the tables, the median leveraged beta in all the analyzed years is more than one. Assuming that the US companies from which the betas are calculated have an average debt and distribution of companies between industries similar to that of all Slovenian companies, we can deduce that in the median our analyzed companies are more risky than the median Slovenian company ( Tables 5 and 6 ).

Despite this assumption, which is not likely to be true, the greater ROE of analyzed companies in comparison to the aggregated ROE of all Slovenian companies demonstrate the positive relationship between risk and return. The relative residual income (in %) is negative in the entire analyzed period except in 2016 ( Tables 5 and 6 ).

These results simply indicate that in the median in the period from 2012–2015, capital owners/investors in the analyzed companies were losing the value of their invested equity capital. ( Figure 1 ) Calculations were made based on Damodaran (2020) , Fred (2020) , European Central Bank (2020), Bloomberg (2020) , Gvin (2020) .

But looking at the aggregate ROE of all Slovenian companies we can also assume that on average all Slovenian companies performed below the required return in the same period and consequently have had negative residual income in that period. However, the results have a positive trend in the analyzed period due to the increasing ROE and the decreasing required return on equity (mostly due to decreased risk) ( Figure 1 ).

Based on financial data about company performance and results in the previous chapters we have researched the connection between the instrumental aspects of core business process control activities and ROE- r and ROE-a. According to the aim of our research, to relate instrumental aspects of core business processes to company risk-adjusted performance, further on we have based these on calculated ROE- r and ROE-a and relate them to the instrumental aspects of core business processes ( Table 7 ).

Due to the small sample and our decision to exclude 5% of companies with extreme five-years average performance measures (both tails), we then explored the differences in each instrumental aspect of core business process control activities assessments between the best performers 10% (N = 15 companies) and the worst performers 10% (N = 15 companies) based on five-years average company risk-adjusted performance measures ROE- r and ROE-a.

We checked for statistical differences in each instrumental aspect of the core business process control activities assessments between the best 10% and the worst 10% companies based on company risk-adjusted performance measures. We used a non-parametric Mann–Whitney U test. The results were not sufficiently significant to statistically prove the difference. Thus, we cannot statistically accept the developed hypothesis:

The instrumental aspect of control as a core process management activity has a statistically significant impact on the company risk-adjusted performance measures ROE- r and ROE-a.

The statistically insignificant differences could be expected due to the small sample size, notwithstanding the fact that there are significant differences between the assessments in all statements (see the assessments in Table 7 ). Nevertheless, we can see an interesting pattern in Table 7 that we did not expect. All statements: “the success of changes to the core processes is measured with the assistance of clearly defined indicators.” “The success of changes to the core processes is measured by financial effects.” “We do not deviate from the set goals of changes to the core processes in our company.” and the statement ''When measuring the success of changes to the core processes, we put short-term (immediate) benefits for the company in the foreground.'' show higher value for companies with lower ROE-a and ROE- r that are, financially speaking, less successful.

In accordance with our theory, we expect a positive connection between BPM and company financial performance. However, even a negative connection could be explained. Business processes changes (process optimization, implementation of new technology, etc.) in less successful companies are subjected to much more scrutiny. Because of the restricted financial position of such companies, they must be more careful in making decisions. Less successful companies have limited access to financial resources so they must be more oriented on quick wins, which pushes them to put the financial effects of decision making on process change in the front line. Of course, stronger evidence needs to be obtained before such a claim can be positively established.

On the other hand, based on the literature review we can conclude that successful companies have a better balance between the instrumental and interest aspects of process management. So, lower values of the instrumental aspect of the core business process are expected from this perspective.

5. Conclusions

This paper research studies the control of core business processes from the instrumental perspective and its effect on the industry-specific financial risk measures of company performance. For this purpose, we developed and tested a measurement instrument for measuring the instrumental aspects of BPM. For the performance measurement of companies, we used the risk-adjusted performance measures ROE- r and ROE-a, which allow a joint analysis on a sample of companies from different industries.

We tested for statistical differences in each instrumental aspect of the core business process control activities assessments between best and worst companies based on company risk-adjusted performance measures. The results were not sufficiently significant to statistically prove the difference and to accept the hypothesis that the instrumental aspect of control as a core process management activity has a statistically significant impact on the company risk-adjusted performance measures ROE- r and ROE-a.

Despite not being statistically proven (presumably due to the small sample size), the results suggest that the instrumental aspects of core business process control activities are negatively correlated to the risk-adjusted company performance measures ROE- r and ROE-a. This suggests that companies with lower ROE- r and ROE-a are more focused on the instrumental aspects of BPM.

In accordance with our theory, we expected a positive connection between BPM and company financial performance. However, we tried to explain a negative connection with the following reasoning. Business processes changes in less successful companies are subjected to much more scrutiny. Because of the restricted financial position of such companies they must be more careful in making decisions as they must be more oriented on quick wins, which push them to put the financial effects of decision making on process change as the main goal. However, stronger evidence needs to be obtained before such a claim can be positively established.

Research implications of the results might concern investors, managers and BPM practitioners on the field of BPM improvements, especially in SMEs. A lack of BPM improvement measurement related to company performance can be the main obstacle of starting BPM improvement initiatives. On the other hand, understanding relations between core business processes and company performance and thus related risk-adjusted measures can direct managers to invest more in BPM at this time and to set priority management actions in the SMEs.

The main limitation of our purposed study model is that we have studied only control activities of core business processes and relate it to company risk-adjusted performance measures. The study has been limited also by the number of SMEs and thus related number of respondents and by the use of a survey as a research instrument. Results of this research could be affected by the possible subjective assessment of respondents (general managers, leaders, process owners) about BPM status in companies. An additional limitation of our research is the degree of reliability implied by the assumptions of the models used to estimate the required ROE and risk, and a certain degree of subjectivity in estimating the variables entering the models. However, we believe that the results are more relevant than they would be using performance measures without risk adjustment. Results of this research in SMEs cannot be generalized for large companies. In large companies, we have to deal with a greater division of work, different approaches to business processes, deeper organizational structures, stronger positions on the market (e.g. against suppliers, customers).

periodical studies on the same population or the sample, with latest company performance data;

studying relationship between factors which were not included now, such as: company size and industry, type of the processes, BPM status in the company, awareness about BPM, position of participants included in the research);

the same study on large companies; and

comparison studies (in time and with similar economies).

This calls for totally new research based on a new questionnaire data collection regarding business processes.

Median relative residual income (ROE- r ) (%) for the analyzed companies in the period 2012–2016

Instrumental aspects of core business processes – control

KMO and Bartlett tests

Correlation between statements: Instrumental aspects of core business processes – control

Estimated parameters

Aggregated ROE of all Slovenian companies and median ROE of all analyzed companies

Median values of estimated parameters

Comparison of instrumental aspects of core business process control activities to ROE- r and ROE-a

For example, in the following finance literature: ( Arnold, 2013 ; Brealey et al. , 2012 ; Brigham and Ehrhardt, 2017 ; Damodaran, 2016 ).

Refer to Feltham and Ohlson (1996 ), Dechow et al. (1999 ), Biddle et al. (2001 ), Halsey (2001 ), Lundholm and O’Keefe (2001 ), Cheng (2005 ), Jamin (2005 ), Balachandran and Mohanram (2012 ).

Blitz et al. (2014 ) have combined the CAPM assumptions into the following groups: there are no constraints (e.g. on leverage and short-selling); investors are risk averse, maximise the expected utility of absolute wealth and care only about the mean and variance of return; there is only one period; information is complete and rationally processed; and markets are perfect (i.e. all assets are perfectly divisible and perfectly liquid, there are no transaction costs, there are no taxes, and all investors are price takers).

See Brigham and Ehrhardt (2011 ), Wright et al. (2003 ).

The Slovenian capital market is not efficient. Total market capitalisation of Ljubljana Stock Exchange (Ljubljana Stock Exchange, 2017 ) at the end of the year 2017 was €5.3bn, the annual turnover around €350m. The market is small, with only nine actively traded stocks in the first quotation, and twenty stocks traded in the standard quotation.

Data from Fred (2020 ).

The European Central Bank (2020 ) aims at inflation rates of below, but close to 2% over the medium term.

We retrieved the data for all variables that we estimated from Damodaran (2020 ) except for the Slovenia net of United States credit default swap.

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Further reading

Kohlbacher , M. ( 2010 ), “ The effects of process orientation: a literature review ”, Business Process Management Journal , Vol. 16 No. 1 , pp. 135 - 152 .

Lau , H. , Dilupa , N. , Premeratne , S. and Shum , P.K. ( 2016 ), “ BPM for supporting customer relationship and profit decision ”, Business Process Management Journal , Vol. 22 No. 1 , pp. 231 - 255 .

Macedo de Morais , R. , Samir , K. , Dallavalle de Paadua , S.I. and Costa Lucirton , A. ( 2014 ), “ An analysis of BPM lifecycles: from a literature review to a framework proposal ”, Business Process Management Journal , Vol. 20 No. 3 , pp. 412 - 432 .

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